liu.seSearch for publications in DiVA
Endre søk
Begrens søket
12 1 - 50 of 56
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    An Inertial Navigation Framework for Indoor Positioning with Robust HeadingManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Indoor localization in unknown environments is considered, using inertial measurements from accelerometers, gyroscopes and magnetometers. Foot-mounted inertial sensors allow for stand-still detection triggering zero velocity updates that reduces the inertial navigation system (ins) drift in distance traveled from cubical to linear in time. We present a statistical framework, based on an navigation model. The standard stand-still mode is complemented with binary modes of magnetic disturbances. Test statistics for these two mode estimation problems are derived. Instead of making hard decisions, a hidden Markov model filter is used to compute the mode probabilities, leading to soft measurement updates in the Kalman filter.

    Based on this, a robust smoothed heading estimate is computed in a second stage using the magnetometer. The final position estimate is then obtained by fusing the ins output with the robust heading in a standard dead-reckoning filter. Experiments demonstrate that the robust heading decreases the relative error in position from 10% to less than 1%, despite large magnetic disturbances.

  • 2.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Probabilistic Stand Still Detection using Foot Mounted IMU2010Inngår i: Proceedings of the 13th International Conference on Information Fusion, 2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We consider stand still detection for indoor localization based on observations from a foot-mounted inertial measurement unit (IMU). The main contribution is a statistical framework for stand-still detection, which is a fundamental step in zero velocity update (ZUPT) to reduce the drift from cubic to linear in time. First, the observations are transformed to a test statistic having non-central chi-square distribution during zero velocity. Second, a hidden Markov model is used to describe the mode switching between stand still, walking, running, crawling and other possible movements. The resulting algorithm computes the probability of being in each mode, and it is easily extendable to a dynamic navigation framework where map information can be included. Results of first mode probability estimation, second map matching without ZUPT and third step length estimation with ZUPT are provided.

  • 3.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Robust Heading Estimation Indoors2013Rapport (Annet vitenskapelig)
    Abstract [en]

    Indoor positioning in unknown environments is crucial for rescue personnel and future infotainment systems. Dead-reckoning inertial sensor data gives accurate estimate of distance, for instance using zero velocity updates, while the heading estimation problem is inherently more difficult due to the large degree of magnetic disturbances indoors. We propose a Kalman filter bank approach based on supporting a magnetic compass with gyroscope turn rate information, where a hidden Markov model is used to model the presence of magnetic disturbances. In parallel, we suggest to run a robust heading estimation system based on data from a sliding window. The robust estimate is used to detect filter divergence, and to restart the filter when needed. The underlying assumptions and the heading estimation performance are supported in field trials using more than 500 data sets from more than 50 venues in 5 continents.

  • 4.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Robust Heading Estimation Indoors using Convex Optimization2013Inngår i: 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2013, s. 1173-1179Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The problem of estimating heading is central in the indoor positioning problem based on measurements from inertial measurement and magnetic units, Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but m here long segments of data are useless in practice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

  • 5.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Robust Heading Estimation Indoors using Convex Optimization2013Rapport (Annet vitenskapelig)
    Abstract [en]

    The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

  • 6.
    Callmer, Jonas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Svensson, Henrik
    Nira Dynamics, Sweden.
    Carlbom, Pelle
    Saab Dynamics AB, Sweden.
    RADAR SLAM using Visual Features2011Inngår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2011, nr 71Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A vessel navigating in a critical environment such as an archipelago, requires very accurate movement estimates. Intentional or unintentional jamming makes gps unreliable as the only source of information and an additional independent navigation system should be used. In this paper we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a complete navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.

  • 7.
    Grönwall, Christina
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Larsson, Håkan
    FOI.
    Engström, Philip
    FOI.
    Concurrent object recognition and localization for first responder applications2013Konferansepaper (Annet vitenskapelig)
  • 8.
    Gunnarsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geijer Lundin, Erik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bark, Gunnar
    Ericsson Research, Sweden.
    Wiberg, Niclas
    Ericsson Research, Sweden.
    Englund, Eva
    Ericsson Research, Sweden.
    Uplink Transmission Timing in WCDMA2003Inngår i: Proceedings of the 58th IEEE Vehicular Technology Conference, 2003, s. 1688-1692 vol.3Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In wireless network uplink communications, there is a trade off between transmission coordination to avoid overload situations, and distributed transmission decisions to adapt to fast channel variations. Here, uplink transmission timing (UTT) is proposed as a scheme to allow some load control support, while transmitting mainly when the channel is favorable. It utilizes channel state feedback in the form of power control commands, which already are available in the system. Simulations illustrate the transmission timing behavior, and also indicate that UTT is a power and intercell interference efficient scheme to transport data compared to traditional dedicated channels with continuous transmissions and to schemes where transmission decisions are random.

  • 9.
    Gunnarsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geijer Lundin, Erik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bark, Gunnar
    Ericsson Research, Sweden.
    Wiberg, Niclas
    Ericsson Research, Sweden.
    Englund, Eva
    Ericsson Research, Sweden.
    Uplink Transmission Timing in WCDMA2003Rapport (Annet vitenskapelig)
    Abstract [en]

    In wireless network uplink communications, there is a trade off between transmission coordination to avoid overload situations, and distributed transmission decisions to adapt to fast channel variations. Here, uplink transmission timing (UTT) is proposed as a scheme to allow some load control support, while transmitting mainly when the channel is favorable. It utilizes channel state feedback in the form of power control commands, which already are available in the system. Simulations illustrate the transmission timing behavior, and also indicate that UTT is a power and intercell interference efficient scheme to transport data compared to traditional dedicated channels with continuous transmissions and to schemes where transmission decisions are random.

  • 10.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geijer Lundin, Erik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Transmission Timing - A Control Approach to Distributed Uplink Scheduling in WCDMA2004Inngår i: Proceedings of Reglermöte 2004, 2004Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Centralized control and coordination of the connections in a wireless network is not possible in practice. To keep the delay from measure-ment instants to actuating the decisions, distributed control is required. This paper focuses on the uplink (from mobiles to base stations) and dis-cusses distributing the decision of when and when not to transmit data (distributed scheduling) to the mobiles. The scheme, uplink transmission timing, utilizes mobile transmitter power control feedback from the base station receiver to determine whether the channel is favorable or not compared to the average channel condition. Thereby, the battery consumption and disturbing power to other connections are reduced. The algorithm can be described as a feedback control system. Some transient behaviors are analyzed using systems theory, and supported by wireless network simulations of a system with a WCDMA (Wideband Code Division Multiple Access) radio interface as in most 3G systems.

  • 11.
    Hanning, Gustav
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Forslöw, Nicklas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Forssén, Per-Erik
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Ringaby, Erik
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Callmer, Jonas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Stabilizing Cell Phone Video using Inertial Measurement Sensors2011Inngår i: The Second IEEE International Workshop on Mobile Vision, Barcelona Spain, 2011, s. 1-8Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    We present a system that rectifies and stabilizes video sequences on mobile devices with rolling-shutter cameras. The system corrects for rolling-shutter distortions using measurements from accelerometer and gyroscope sensors, and a 3D rotational distortion model. In order to obtain a stabilized video, and at the same time keep most content in view, we propose an adaptive low-pass filter algorithm to obtain the output camera trajectory. The accuracy of the orientation estimates has been evaluated experimentally using ground truth data from a motion capture system. We have conducted a user study, where the output from our system, implemented in iOS, has been compared to that of three other applications, as well as to the uncorrected video. The study shows that users prefer our sensor-based system.

  • 12.
    Isaksson, Alf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. ABB AB, Sweden.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Sjöberg, Johan
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ljung, Lennart
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization2009Inngår i: Proceedings of the 41st ISCIE International Symposium on Stochastic Systems, Institute of Systems, Control and Information Engineers , 2009, s. 1-6Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.

  • 13.
    Isaksson, Alf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Sjöberg, Johan
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ljung, Lennart
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization2010Rapport (Annet vitenskapelig)
    Abstract [en]

    In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. A procedure how to approximate the bias correction for nonlinear systems is outlined.

  • 14.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Conte, Gianpaolo
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerad datorsystem. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application2008Inngår i: Proceedings of Reglermöte 2008, 2008, s. 313-322Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

  • 15.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Conte, Gianpaolo
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerad datorsystem. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application2008Inngår i: Proceedings of the 2008 IEEE Aerospace Conference, 2008, s. 1-10Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. The second one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle models are computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

  • 16.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Conte, Gianpolo
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerad datorsystem. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application2008Rapport (Annet vitenskapelig)
    Abstract [en]

    This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

  • 17.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hansson, Anders
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Svante
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Automatic Control Project Course: A Positioning and Control Application for an Unmanned Aerial Vehicle2006Rapport (Annet vitenskapelig)
    Abstract [en]

    In the Conceive – Design – Implement - Operate (CDIO) project course in automatic control, an autonomous unmanned aerial vehicle (UAV) is constructed, utilising an existing radio controlled model aircraft. By adding an inertial sensor that measures acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an onboard computer to calculate control surface signals to a set of servos in order to follow a predefined way-point trajectory. The project involves 17 students and comprises both positioning, control and hardware design. The main pedagogical goal is for students to apply their theoretical knowledge within a project framework in order to improve important aspects of their engineering skills in a realistic manner.

  • 18.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hansson, Anders
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Svante
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Automatic Control Project Course: A Positioning and Control Application for an Unmanned Aerial Vehicle2006Inngår i: World Transactions on Engineering and Technology Education, ISSN 1446-2257, Vol. 5, s. 291-294Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the Conceive – Design – Implement - Operate (CDIO) project course in automatic control, an autonomous unmanned aerial vehicle (UAV) is constructed, utilising an existing radio controlled model aircraft. By adding an inertial sensor that measures acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an onboard computer to calculate control surface signals to a set of servos in order to follow a predefined way-point trajectory. The project involves 17 students and comprises both positioning, control and hardware design. The main pedagogical goal is for students to apply their theoretical knowledge within a project framework in order to improve important aspects of their engineering skills in a realistic manner.

  • 19.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Sjöberg, Johan
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hol, Jeroen
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hansson, Anders
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Positioning and Control of an Unmanned Aerial Vehicle2006Inngår i: Proceedings of the 2nd International CDIO Conference and Collaborators' Meeting, 2006Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In the CDIO-project course in Automatic Control, an Autonomous Unmanned Aerial vehicle (UAV) is constructed, utilizing an existing radio controlled model aircraft. By adding an inertial sensor measuring acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an on board computer to calculate rudder control signals to a set of DC-servos in order to follow a predefined way-point trajectory. The project involves 17 students, which is roughly three times as big as previous projects, and it comprises both positioning, control, and hardware design. Since the project is still ongoing some preliminary results and conclusions are presented.

  • 20.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Sjöberg, Johan
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hol, Jeroen
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hansson, Anders
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Positioning and Control of an Unmanned Aerial Vehicle2006Rapport (Annet vitenskapelig)
    Abstract [en]

    In the CDIO-project course in Automatic Control, an Autonomous Unmanned Aerial vehicle (UAV) is constructed, utilizing an existing radio controlled model aircraft. By adding an inertial sensor measuring acceleration and rotation, together with a Global Positioning System (GPS) sensor, the aim is to construct an accurate positioning system. This is used by an on board computer to calculate rudder control signals to a set of DC-servos in order to follow a predefined way-point trajectory. The project involves 17 students, which is roughly three times as big as previous projects, and it comprises both positioning, control, and hardware design. Since the project is still ongoing some preliminary results and conclusions are presented.

  • 21.
    Lindsten, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Callmer, Jonas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ohlsson, Henrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geo-Referencing for UAV Navigation using Environmental Classification2010Rapport (Annet vitenskapelig)
    Abstract [en]

    A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

  • 22.
    Lindsten, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Callmer, Jonas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ohlsson, Henrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geo-Referencing for UAV Navigation using Environmental Classification2010Inngår i: Proceedings of the 2010 IEEE International Conference on Robotics and Automation, 2010, s. 1420-1425Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

  • 23.
    Orguner, Umut
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Skoglar, Per
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Combined Point Mass and Particle Filter for Target Tracking2010Inngår i: Proceedings of the 2010 IEEE Aerospace Conference, 2010Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a combined Point Mass Filter (PMF) and Particle Filter (PF), which utilizes the support of the PMF and the high particle density in the PF close to the current estimate. The result is a filter robust to unexpected process events but still with low error covariance. This filter is especially useful for target tracking applications, where target maneuvers suddenly can change unpredictably.

  • 24.
    Ovrén, Hannes
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Forssén, Per-Erik
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Improving RGB-D Scene Reconstruction using Rolling Shutter Rectification2015Inngår i: New Development in Robot Vision / [ed] Yu Sun, Aman Behal & Chi-Kit Ronald Chung, Springer Berlin/Heidelberg, 2015, s. 55-71Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Scene reconstruction, i.e. the process of creating a 3D representation (mesh) of some real world scene, has recently become easier with the advent of cheap RGB-D sensors (e.g. the Microsoft Kinect).

    Many such sensors use rolling shutter cameras, which produce geometrically distorted images when they are moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans.We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor.

    For scene reconstruction we use the Kinect Fusion algorithm to produce meshes. We create meshes from both raw and rectified depth scans, and these are then compared to a ground truth mesh. The types of motion we investigate are: pan, tilt and wobble (shaking) motions.

    As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.

    This chapter is an extension of a paper at the IEEE Workshop on Robot Vision [10]. Compared to that paper, we have improved the rectification to also correct for lens distortion, and use a coarse-to-fine search to find the time shift more quicky.We have extended our experiments to also investigate the effects of lens distortion, and to use more accurate ground truth. The experiments demonstrate that correction of rolling shutter effects yields a larger improvement of the 3D model than correction for lens distortion.

  • 25.
    Ovrén, Hannes
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Forssén, Per-Erik
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Why Would I Want a Gyroscope on my RGB-D Sensor?2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Many RGB-D sensors, e.g. the Microsoft Kinect, use rolling shutter cameras. Such cameras produce geometrically distorted images when the sensor is moving. To mitigate these rolling shutter distortions we propose a method that uses an attached gyroscope to rectify the depth scans. We also present a simple scheme to calibrate the relative pose and time synchronization between the gyro and a rolling shutter RGB-D sensor. We examine the effectiveness of our rectification scheme by coupling it with the the Kinect Fusion algorithm. By comparing Kinect Fusion models obtained from raw sensor scans and from rectified scans, we demonstrate improvement for three classes of sensor motion: panning motions causes slant distortions, and tilt motions cause vertically elongated or compressed objects. For wobble we also observe a loss of detail, compared to the reconstruction using rectified depth scans. As our method relies on gyroscope readings, the amount of computations required is negligible compared to the cost of running Kinect Fusion.

  • 26.
    Rantakokko, Jouni
    et al.
    Royal Institute of Technology, Sweden.
    Rydell, Joakim
    Royal Institute of Technology, Sweden.
    Stromback, Peter
    Royal Institute of Technology, Sweden.
    Handel, Peter
    Royal Institute of Technology, Sweden.
    Callmer, Jonas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Jobs, Magnus
    Uppsala University, Sweden.
    Gruden, Mathias
    Uppsala University, Sweden.
    Accurate and Reliable Soldier and First Responder Indoor Positioning: Multi-Sensor Systems and Cooperative Localization2011Inngår i: IEEE wireless communications, ISSN 1536-1284, E-ISSN 1558-0687, Vol. 18, nr 2, s. 10-18Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A robust, accurate positioning system with seamless outdoor and indoor coverage is a highly needed tool for increasing safety in emergency response and military urban operations. It must be lightweight, small, inexpensive, and power efficient, and still provide meter-level accuracy during extended operations. GPS receivers, inertial sensors, and local radio-based ranging are natural choices for a multisensor positioning system. Inertial navigation with foot-mounted sensors is suitable as the core system in GPS denied environments, since it can yield meter-level accuracies for a few minutes. However, there is still a need for additional supporting sensors to keep the accuracy at acceptable levels during the duration of typical soldier and first responder operations. Suitable aiding sensors are three-axis magnetometers, barometers, imaging sensors, Doppler radars, and ultrasonic sensors. Furthermore, cooperative positioning, where first responders exchange position and error estimates in conjunction with performing radio-based ranging, is deemed a key technology. This article provides a survey on technologies and concepts for high accuracy soldier and first responder positioning systems, with an emphasis on indoor positioning.

  • 27.
    Schön, Thomas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Framework for Simultaneous Localization and Mapping Utilizing Model Structure2007Inngår i: Proceedings of the 10th International Conference on Information Fusion, 2007, s. 1-8Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. The second one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. An algorithm is introduced, which merges FastSLAM and MPF, and the result is an MPF algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a 3D SLAM development environment, fusing measurements from inertial sensors (accelerometer and gyro) and vision are presented.

  • 28.
    Schön, Thomas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Framework for Simultaneous Localization and Mapping Utilizing Model Structure2007Rapport (Annet vitenskapelig)
    Abstract [en]

    The basic nonlinear ltering problem for dynamical systems is considered. Approximating the optimal lter estimate by particle lter methods has become perhaps the most common and useful method in recent years. Many variants of particle lters have been suggested, and there is an extensive lit- erature on the theoretical aspects of the quality of the approximation. Still,a clear cut result that the approximate solution, for unbounded functions, converges to the true optimal estimate as the number of particles tends to innity seems to be lacking. It is the purpose of this contribution to give such a basic convergence result.

  • 29.
    Schön, Thomas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Fast Particle Filters for Multi-Rate Sensors2007Rapport (Annet vitenskapelig)
    Abstract [en]

    Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

  • 30.
    Schön, Thomas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Fast Particle Filters for Multi-Rate Sensors2007Inngår i: Proceedings of the 15th European Signal Processing Conference, 2007Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

  • 31.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Pedestrian Tracking with an Infrared Sensor using Road Network Information2012Inngår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 1, nr 26, s. 2012a-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article presents a pedestrian tracking methodology using an infrared sensor for surveillance applications. A distinctive feature of this study compared to the existing pedestrian tracking approaches is that the road network information is utilized for performance enhancement. A multiple model particle filter, which uses two different motion models, is designed for enabling the tracking of both road-constrained (on-road) and unconstrained (off-road) targets. The lateral position of the pedestrians on the walkways are taken into account by a specific on-road target model. The overall framework seamlessly integrates the negative information of occlusion events into the algorithm for which the required modifications are discussed. The resulting algorithm is illustrated on real data from a field trial for different scenarios.

  • 32.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Target Search and Tracking with Gimballed Vision Sensor on a UAV2012Rapport (Annet vitenskapelig)
    Abstract [en]

    This work considers a sensor management problem where a number of road bounded vehicles are monitored by a UAV with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

  • 33.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle2012Inngår i: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 4, nr 7, s. 2076-2111Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

  • 34.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Target Tracking with an Approximative Rao-Blackwellized Particle Filter2009Inngår i: Proceedings from the 12th International Conference on Information Fusion, 6-9 July, Seattle, Washington, USA, IEEE conference proceedings, 2009, s. 17-24Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Using prior information about the road network will improve the estimation performance for a road constrained target significantly. Several estimation methods have been proposed to handle the multi-modality that arise in a road target tracking application. One popular filter suitable for this kind of non-linear problems is the Particle Filter, but a major drawback is that the Particle filter requires a large amount of particles as the state dimension increases to maintain a good approximation of the filtering distribution. In this paper a Rao-Blackwellized Particle Filter based approach is proposed to reduce the dimension of the state space in road target tracking applications. Furthermore, it is also shown how prior information about the probability of detection can be used to improve the estimation performance further.

  • 35.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Simultaneous Camera Orientation Estimation and Road Target Tracking2012Inngår i: Proceedings of the 15th International Conference on Information Fusion, IEEE , 2012, s. 802-807Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

  • 36.
    Skoglar, Per
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Simultaneous Camera Orientation Estimation and Road Target Tracking2012Rapport (Annet vitenskapelig)
    Abstract [en]

    Airborne surveillance systems equipped with a vision/infrared camera require good knowledge about the position and orientation of the camera for successful tracking of ground targets. In particular, this is essential when incorporating prior information, like road maps, that is expressed relative a global reference system. Usually, it is possible to obtain good positioning with inertial/satellite navigation systems, but estimating the orientation is generally more difficult. It might be possible to use SLAM (Simultaneous Localization and Mapping) or image registration approaches to support the navigation system, but not always since such approaches require stable features in the images. In this paper the problem of simultaneous orientation error estimation and road target tracking is considered by assuming that the target is constrained to a known road network. A particle filter approach is proposed and it is shown that the result of this filter is close to the performance of the ideal case where the orientation error is perfectly known. However, the performance depends on how informative the road path is and in rare cases the orientation error is unobservable.

  • 37.
    Tisdale, John
    et al.
    University of California, Berkeley, CA, USA.
    Ryan, Allison
    University of California, Berkeley, CA, USA.
    Kim, Zu
    University of California, Berkeley, CA, USA.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hedrick, Karl
    University of California, Berkeley, CA, USA.
    A Multiple UAV System for Vision-Based Search and Localization2008Rapport (Annet vitenskapelig)
    Abstract [en]

    The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

    Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

  • 38.
    Tisdale, John
    et al.
    University of California, Berkeley, CA, USA.
    Ryan, Allison
    University of California, Berkeley, CA, USA.
    Kim, Zu
    University of California, Berkeley, CA, USA.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hedrick, Karl
    University of California, Berkeley, CA, USA.
    A Multiple UAV System for Vision-Based Search and Localization2008Inngår i: Proceedings of the '08 American Control Conference, IEEE , 2008, s. 1985-1990Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

    Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

  • 39.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation and Detection with Applications to Navigation2008Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection.

    The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution.

    Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably.

    Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

    Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined.

    Delarbeid
    1. Particle Filter SLAM with High Dimensional Vehicle Model
    Åpne denne publikasjonen i ny fane eller vindu >>Particle Filter SLAM with High Dimensional Vehicle Model
    2009 (engelsk)Inngår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 55, nr 4, s. 249-266Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    This work presents a particle filter (PF) method closely related to FastSLAM for solving the simultaneous localization and mapping (SLAM) problem. Using the standard FastSLAM algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from a UAV (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the SLAM problem.

    sted, utgiver, år, opplag, sider
    Springer Netherlands, 2009
    Emneord
    Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-15500 (URN)10.1007/s10846-008-9301-y (DOI)
    Prosjekter
    CADICS
    Tilgjengelig fra: 2008-11-12 Laget: 2008-11-12 Sist oppdatert: 2017-12-14bibliografisk kontrollert
    2. Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application
    Åpne denne publikasjonen i ny fane eller vindu >>Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application
    2008 (engelsk)Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Artikkel i tidsskrift (Fagfellevurdert) Submitted
    Abstract [en]

    Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

    Emneord
    Fault detection, Parity space sensor fusion, Inertial sensors, Magnetometer
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-15501 (URN)
    Tilgjengelig fra: 2008-11-12 Laget: 2008-11-12 Sist oppdatert: 2017-12-14bibliografisk kontrollert
    3. Detecting Spurious Features using Parity Space
    Åpne denne publikasjonen i ny fane eller vindu >>Detecting Spurious Features using Parity Space
    2008 (engelsk)Inngår i: Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision, 2008, s. 353-358Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Detection of spurious features is instrumental in many computer vision applications. The standard approach is feature based, where extracted features are matched between the image frames. This approach requires only vision, but is computer intensive and not yet suitable for real-time applications. We propose an alternative based on algorithms from the statistical fault detection literature. It is based on image data and an inertial measurement unit (IMU). The principle of analytical redundancy is applied to batches of measurements from a sliding time window. The resulting algorithm is fast and scalable, and requires only feature positions as inputs from the computer vision system. It is also pointed out that the algorithm can be extended to also detect nonstationary features (moving targets for instance). The algorithm is applied to real data from an unmanned aerial vehicle in a navigation application.

    Emneord
    Detection, Vision, Parity space, Inertial sensors
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-15502 (URN)10.1109/ICARCV.2008.4795545 (DOI)978-1-4244-2287-6 (ISBN)978-1-4244-2286-9 (ISBN)
    Konferanse
    International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, December, 2008
    Tilgjengelig fra: 2008-11-12 Laget: 2008-11-12 Sist oppdatert: 2013-02-23bibliografisk kontrollert
    4. Fast Particle Filters for Multi-Rate Sensors
    Åpne denne publikasjonen i ny fane eller vindu >>Fast Particle Filters for Multi-Rate Sensors
    2007 (engelsk)Inngår i: Proceedings of the 15th European Signal Processing Conference, 2007Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

    Emneord
    Nonlinear filters, Particle filter, Kalman filter, Multi-rate sensors
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-15503 (URN)
    Konferanse
    15th European Signal Processing Conference, Poznan, Poland, September, 2007
    Tilgjengelig fra: 2008-11-12 Laget: 2008-11-12 Sist oppdatert: 2013-02-26bibliografisk kontrollert
    5. A Multiple UAV System for Vision-Based Search and Localization
    Åpne denne publikasjonen i ny fane eller vindu >>A Multiple UAV System for Vision-Based Search and Localization
    Vise andre…
    2008 (engelsk)Inngår i: Proceedings of the '08 American Control Conference, IEEE , 2008, s. 1985-1990Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

    Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

    sted, utgiver, år, opplag, sider
    IEEE, 2008
    Emneord
    Bayes methods, Aerospace control, Aircraft, Cameras, Particle filtering
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-15504 (URN)10.1109/ACC.2008.4586784 (DOI)978-1-4244-2079-7 (ISBN)978-1-4244-2078-0 (ISBN)
    Konferanse
    '08 American Control Conference, Seattle, WA, USA, June, 2008
    Tilgjengelig fra: 2008-11-12 Laget: 2008-11-12 Sist oppdatert: 2013-12-03bibliografisk kontrollert
  • 40.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Initial State Estimation for Fault Detection over Sliding Windows2006Inngår i: Proceedings of Reglermöte 2006, 2006Konferansepaper (Annet vitenskapelig)
  • 41.
    Törnqvist, David
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Statistical Fault Detection with Applications to IMU Disturbances2006Licentiatavhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.

    In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.

    The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.

    The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

  • 42.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geijer Lundin, Erik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Transmission Timing - A Control Approach to Distributed Uplink Scheduling in WCDMA2004Inngår i: Proceedings of the 2004 American Control Conference, 2004, s. 1667-1672 vol.2Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Centralized control and coordination of the connections in a wireless network is not possible in practice. To keep the delay from measurement instants to actuating the decisions, distributed control is required. This paper focuses on the uplink (from mobiles to base stations) and discusses distributing the decision of when and when not to transmit data (distributed scheduling) to the mobiles. The scheme, uplink transmission timing, utilizes mobile transmitter power control feedback from the base station receiver to determine whether the channel is favorable or not compared to the average channel condition. Thereby, the battery consumption and disturbing power to other connections are reduced. The algorithm can be described as a feedback control system. Some transient behaviors are analyzed using systems theory, and supported by wireless network simulations of a system with a WCDMA (Wideband Code Division Multiple Access) radio interface as in most 3G systems.

  • 43.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Geijer Lundin, Erik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Transmission Timing - A Control Approach to Distributed Uplink Scheduling in WCDMA2004Rapport (Annet vitenskapelig)
    Abstract [en]

    Centralized control and coordination of the connections in a wireless network is not possible in practice. To keep the delay from measure-ment instants to actuating the decisions, distributed control is required. This paper focuses on the uplink (from mobiles to base stations) and dis-cusses distributing the decision of when and when not to transmit data (distributed scheduling) to the mobiles. The scheme, uplink transmission timing, utilizes mobile transmitter power control feedback from the base station receiver to determine whether the channel is favorable or not compared to the average channel condition. Thereby, the battery consumption and disturbing power to other connections are reduced. The algorithm can be described as a feedback control system. Some transient behaviors are analyzed using systems theory, and supported by wireless network simulations of a system with a WCDMA (Wideband Code Division Multiple Access) radio interface as in most 3G systems.

  • 44.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Eliminating the Initial State for the Generalized Likelihood Ratio Test2006Inngår i: Proceedings of the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2006, s. 699-604Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence ofinitial state is removed by projection, and the other on using prior information obtained by Kalman filtering past data. A new idea of anti-causal Kalman filtering in the present data batch is introduced and compared to the previous methods. An efficient parameterization of incipient faults is given. It is shown in simulations of torque disturbances on a DCmotor that efficient fault profile parameterization and using smoothed estimates of the initial state increase performance considerably. (Copyright 2006 IFAC)

  • 45.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Eliminating the Initial State for the Generalized Likelihood Ratio Test2006Rapport (Annet vitenskapelig)
    Abstract [en]

    Fault detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. There are two standard approaches to this problem. One is based on a parity space, where the influence ofinitial state is removed by projection, and the other on using prior information obtained by Kalman filtering past data. A new idea of anti-causal Kalman filtering in the present data batch is introduced and compared to the previous methods. An efficient parameterization of incipient faults is given. It is shown in simulations of torque disturbances on a DCmotor that efficient fault profile parameterization and using smoothed estimates of the initial state increase performance considerably.

  • 46.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application2008Rapport (Annet vitenskapelig)
    Abstract [en]

    Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

  • 47.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application2008Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

  • 48.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Klein, Inger
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    GLR Tests for Fault Detection over Sliding Data Windows2005Inngår i: Proceedings of the 16th IFAC World Congress, 2005, s. 124-124Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

  • 49.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Klein, Inger
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    GLR Tests for Fault Detection over Sliding Data Windows2004Rapport (Annet vitenskapelig)
    Abstract [en]

    The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

  • 50.
    Törnqvist, David
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Klein, Inger
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    GLR Tests for Fault Detection over Sliding Data Windows2005Rapport (Annet vitenskapelig)
    Abstract [en]

    The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

12 1 - 50 of 56
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf