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Ho, D., Linder, J., Hendeby, G. & Enqvist, M. (2017). Mass estimation of a quadcopter using IMU data. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), June 13-16, 2017, Miami, FL, USA: . Paper presented at 2017 International Conference on Unmanned Aircraft Systems (ICUAS), June 13-16, 2017, Miami, FL, USA (pp. 1260-1266). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Mass estimation of a quadcopter using IMU data
2017 (English)In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), June 13-16, 2017, Miami, FL, USA, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1260-1266Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, an approach to estimate the mass of a quadcopter using only inertial measurements and pilot commands is presented. For this purpose, a lateral dynamic model describing the relation between the roll rate and the lateral acceleration is formulated. Due to the quadcopter’s inherent instability, a controller is used to stabilize the system and the data is collected in closed loop. Under the effect of feedback and disturbances, the inertial measurements used as input and output are correlated with the disturbances, which complicates the parameter estimation. The parameters of the model are estimated using several methods. The simulation and experimental results show that the instrumental-variable method has the best potential to estimate the mass of the quadcopter in this setup.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-139795 (URN)10.1109/ICUAS.2017.7991417 (DOI)000425255200155 ()9781509044955 (ISBN)9781509044962 (ISBN)
Conference
2017 International Conference on Unmanned Aircraft Systems (ICUAS), June 13-16, 2017, Miami, FL, USA
Projects
MarineUAS
Funder
EU, Horizon 2020, 642153
Note

Funding agencies: European Unions Horizon research and innovation programme under the Marie Sklodowska-Curie grant [642153]

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-03-21Bibliographically approved
Olofsson, J., Veibäck, C., Hendeby, G. & Johansen, T. A. (2017). Outline of a System for Integrated Adaptive Ice Tracking and Multi-Agent Path Planning. In: Proceedings of the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS): . Paper presented at The 4th Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), Linköping, Sweden, October 3-5, 2017 (pp. 13-18). IEEE
Open this publication in new window or tab >>Outline of a System for Integrated Adaptive Ice Tracking and Multi-Agent Path Planning
2017 (English)In: Proceedings of the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), IEEE, 2017, p. 13-18Conference paper, Published paper (Refereed)
Abstract [en]

In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and winds of high enough resolution. Thus, observations of drift ice is essential to an up-to-date ice-tracking estimate.

Recent years have seen the rise of Unmanned Aerial Systems (UAS) as a platform for geoobservation, and so too for the tracking of sea ice. Being a mobile platform, the research on UAS path-planning is extensive and usually involves an objective-function to minimize. For the purpose of observation however, the objective-function typically changes as observations are made along the path.

Further, the general problem involves multiple UAS and—in the case of sea ice tracking—vast geographical areas.

In this paper we discuss the architectural outline of a system capable of fusing data from multiple sources—UAS’s and others—as well as incorporating that data for both path-planning, sea ice movement prediction and target initialization. The system contains tracking of sea ice objects, situation map logic and is expandable as discussed with path-planning capabilities for closing the loop of optimizing paths for information acquisition.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Ice Tracking, UAS, Sensor Fusion, Path Planning
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-141910 (URN)10.1109/RED-UAS.2017.8101636 (DOI)000427383700003 ()978-1-5386-0939-2 (ISBN)978-1-5386-0940-8 (ISBN)
Conference
The 4th Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), Linköping, Sweden, October 3-5, 2017
Projects
LINK-SIC
Funder
VINNOVA
Note

Funding agencies: European Unions Horizon research and innovation programme under the Marie Sklodowska-Curie grant [642153]; Research Council of Norway through the Centres of Excellence funding scheme [223254 - NTNU-AMOS]; Vinnova Industry Excellence Center LINK-SIC, the S

Available from: 2017-10-13 Created: 2017-10-13 Last updated: 2018-04-11Bibliographically approved
Ho, D., Linder, J., Hendeby, G. & Enqvist, M. (2017). Vertical modeling of a quadcopter for mass estimation and diagnosis purposes. In: Proceedings of the Workshop on Research, Education and Development on Unmanned Aerial Systems, RED-UAS, Linköping, Sweden, 3-5 October, 2017: . Paper presented at Workshop on Research, Education and Development on Unmanned Aerial Systems, RED-UAS, Linköping, Sweden, 3-5 October, 2017. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Vertical modeling of a quadcopter for mass estimation and diagnosis purposes
2017 (English)In: Proceedings of the Workshop on Research, Education and Development on Unmanned Aerial Systems, RED-UAS, Linköping, Sweden, 3-5 October, 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we estimate a model of the vertical dynamics of a quadcopter and explain how this model can be used for mass estimation and diagnosis of system changes. First, a standard thrust model describing the relation between the calculated control signals of the rotors and the thrust that is commonly used in literature is estimated. The estimation results are compared to those using a refined thrust model and it turns out that the refined model gives a significant improvement. The combination of a nonlinear model and closed-loop data poses some challenges and it is shown that an instrumental variables approach can be used to obtain accurate estimates. Furthermore, we show that the refined model opens up for fault detection of the quadcopter. More specifically, this model can be used for mass estimation and also for diagnosis of other parameters that might vary between and during missions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
payload, modeling, quadcopter, fault detection and isolation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-141883 (URN)10.1109/RED-UAS.2017.8101665 (DOI)000427383700032 ()978-1-5386-0939-2 (ISBN)978-1-5386-0940-8 (ISBN)
Conference
Workshop on Research, Education and Development on Unmanned Aerial Systems, RED-UAS, Linköping, Sweden, 3-5 October, 2017
Projects
MarineUAS
Funder
EU, Horizon 2020, 642153
Note

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 642153.

Available from: 2017-10-11 Created: 2017-10-11 Last updated: 2018-04-11Bibliographically approved
Dil, B., Hendeby, G., Gustafsson, F. & Hoenders, B. (2016). Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise. In: Proceedings of the 19th International Conference of Information Fusion: . Paper presented at 19th International Conference of Information Fusion, Heidelberg, Germany, July 5-8 2016 (pp. 521-527). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise
2016 (English)In: Proceedings of the 19th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 521-527Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a diagonal covariance matrix approximation for Wide-Sense Stationary (WSS) signals with correlated Gaussian noise. Existing signal models that incorporate correlations often require regularization of the covariance matrix, so that the covariance matrix can be inverted. The disadvantage of this approach is that matrix inversion is computational intensive and regularization decreases precision. We use Bienayme's theorem to approximate the covariance matrix by a diagonal one, so that matrix inversion becomes trivial, even with nonuniform rather than only uniform sampling that was considered in earlier work. This approximation reduces the computational complexity of the estimator and estimation bound significantly. We numerically validate this approximation and compare our approach with the Maximum Likelihood Estimator (MLE) and Cramer-Rao Lower Bound (CRLB) for multivariate Gaussian distributions. Simulations show that our approach differs less than 0.1% from this MLE and CRLB when the observation time is large compared to the correlation time. Additionally, simulations show that in case of non-uniform sampling, we increase the performance in comparison to earlier work by an order of magnitude. We limit this study to correlated signals in the time domain, but the results are also applicable in the space domain.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Signal processing, approximate estimation
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-130162 (URN)000391273400071 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference of Information Fusion, Heidelberg, Germany, July 5-8 2016
Funder
Swedish Research Council
Available from: 2016-07-12 Created: 2016-07-12 Last updated: 2017-02-03Bibliographically approved
Radnosrati, K., Fritsche, C., Hendeby, G., Gunnarsson, F. & Gustafsson, F. (2016). Fusion of TOF and TDOA for 3GPP Positioning. In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings. Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016 (pp. 1454-1460). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Fusion of TOF and TDOA for 3GPP Positioning
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2016 (English)In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1454-1460Conference paper, Published paper (Refereed)
Abstract [en]

Positioning in cellular networks is often based on mobile-assisted measurements of serving and neighboring base stations. Traditionally, positioning is considered to be enabled when the mobile provides measurements of three different base stations. In this paper, we additionally investigate positioning based on time series of Time Of Flight (TOF) and Time Difference of Arrival (TDOA) measurements gathered from two base stations with known positions, where the specific base stations involved depend on the trajectory of the mobile station.. The set of two base stations is different along the trajectory. Each report contains TOF for the serving base station, and one TDOA measurement for the most favorable neighboring base station relative the serving base station. We derive explicit analytical solution related to the intersection of the absolute distance circle (from TOF) and relative distance hyperbola (from TDOA). We consider both geometric noise-free problem and the more realistic problem with additive noise as delivered in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE). Positioning performance is evaluated using the Cramer-Rao lower bound.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130209 (URN)000391273400193 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Projects
TRAX
Funder
EU, FP7, Seventh Framework Programme, 607400
Available from: 2016-07-15 Created: 2016-07-15 Last updated: 2017-02-03Bibliographically approved
Zhao, Y., Yin, F., Gunnarsson, F., Amirijoo, M. & Hendeby, G. (2016). Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning. In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring): . Paper presented at 2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China (pp. 1-5). IEEE
Open this publication in new window or tab >>Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning
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2016 (English)In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), IEEE , 2016, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-128255 (URN)10.1109/VTCSpring.2016.7504255 (DOI)000386528400206 ()9781509016983 (ISBN)
Conference
2016 IEEE 83rd Vehicular Technology Conference: VTC2016-Spring, 15–18 May 2016, Nanjing, China
Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2019-02-12Bibliographically approved
Kasebzadeh, P., Fritsche, C., Hendeby, G., Gunnarsson, F. & Gustafsson, F. (2016). Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning. In: Proceedings of the 19th International Conference on Information Fusion: . Paper presented at 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016 (pp. 379-385). IEEE conference proceedings
Open this publication in new window or tab >>Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning
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2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, IEEE conference proceedings, 2016, , p. 7p. 379-385Conference paper, Published paper (Refereed)
Abstract [en]

We consider personal navigation systems in devices equipped with inertial sensors and GPS, where we propose an improved Pedestrian Dead Reckoning (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or an indoor positioning system (IPS). A novel filtering approach is proposed that is able to learn internal gait parameters in the PDR algorithm, such as the step length and the step detection threshold. Our approach is based on a multi-rate Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in time intervals when the position is not available, for instance when passing from outdoor to indoor environments where IPS is not available. The effectiveness of the new approach is illustrated on several real world experiments. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 7
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-130174 (URN)000391273400052 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016
Funder
EU, FP7, Seventh Framework Programme, 607400
Available from: 2016-07-13 Created: 2016-07-13 Last updated: 2017-02-03Bibliographically approved
Skoglund, M., Hendeby, G., Nygårds, J., Rantakokko, J. & Eriksson, G. (2016). Indoor Localization Using Multi-Frequency RSS. In: Proceddings of the IEEE/ION Position Location and Navigation Symposium: . Paper presented at IEEE/ION Position Location and Navigation Symposium, Savannah, Georgia, USA, April 11-14, 2016 (pp. 177-186). IEEE conference proceedings
Open this publication in new window or tab >>Indoor Localization Using Multi-Frequency RSS
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2016 (English)In: Proceddings of the IEEE/ION Position Location and Navigation Symposium, IEEE conference proceedings, 2016, p. 177-186Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the usefulness of multi-frequency received signal strength (RSS) for indoor localization. Acollected set of data from four sites containing 7 frequencies fromdual receivers and a high accuracy reference positioning systemis presented. The collected data is also made publicly availablethrough ResearchGate. The data is analyzed with respect tospatial variations using Gaussian processes ( GP ). The resultsshow that there are more rapid signal variations across corridorsthan along them. The uniqueness of RSS fingerprints is analyzedsuggesting that sequences of measurements in smoothing, orsmoothing-like, algorithms that can handle temporary positionambiguities are likely the best choice for localization applications.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Series
IEEE - ION Position Location and Navigation Symposium, ISSN 2153-3598
Keywords
Simultaneous localization and mapping (SLAM), received signal strength (RSS), Gaussian processes (GP)
National Category
Signal Processing Control Engineering Robotics
Identifiers
urn:nbn:se:liu:diva-127138 (URN)10.1109/PLANS.2016.7479774 (DOI)000389021800101 ()9781509020423 (ISBN)
Conference
IEEE/ION Position Location and Navigation Symposium, Savannah, Georgia, USA, April 11-14, 2016
Projects
COOP-LOCLINK-SIC
Funder
Security LinkVINNOVA
Available from: 2016-04-15 Created: 2016-04-15 Last updated: 2017-01-13Bibliographically approved
Roth, M., Hendeby, G. & Gustafsson, F. (2016). Nonlinear Kalman Filters Explained: A Tutorial on Moment Computations and Sigma Point Methods. Journal of Advances in Information Fusion, 11(1), 47-70
Open this publication in new window or tab >>Nonlinear Kalman Filters Explained: A Tutorial on Moment Computations and Sigma Point Methods
2016 (English)In: Journal of Advances in Information Fusion, ISSN 1557-6418, Vol. 11, no 1, p. 47-70Article in journal (Refereed) Published
Abstract [en]

Nonlinear Kalman filters are algorithms that approximately solve the Bayesian filtering problem by employing the measurement update of the linear Kalman filter (KF). Numerous variants have been developed over the past decades, perhaps most importantly the popular sampling based sigma point Kalman filters.In order to make the vast literature accessible, we present nonlinear KF variants in a common framework that highlights the computation of mean values and covariance matrices as the main challenge. The way in which these moment integrals are approximated distinguishes, for example, the unscented KF from the divided difference KF.With the KF framework in mind, a moment computation problem is defined and analyzed. It is shown how structural properties can be exploited to simplify its solution. Established moment computation methods, and their basics and extensions, are discussed in an extensive survey. The focus is on the sampling based rules that are used in sigma point KF. More specifically, we present three categories of methods that use sigma-points 1) to represent a distribution (as in the UKF); 2) for numerical integration (as in Gauss-Hermite quadrature); 3) to approximate nonlinear functions (as in interpolation). Prospective benefits and downsides are listed for each of the categories and methods, including accuracy statements. Furthermore, the related KF publications are listed.The theoretical discussion is complemented with a comparative simulation study on instructive examples.

Place, publisher, year, edition, pages
International society of information fusion, 2016
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-129231 (URN)
Available from: 2016-06-14 Created: 2016-06-14 Last updated: 2017-11-28Bibliographically approved
Veibäck, C., Hendeby, G. & Gustafsson, F. (2016). On Fusion of Sensor Measurements and Observation with Uncertain Timestamp for Target Tracking. In: Proceedings of the 19th International Conference on Information Fusion: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016 (pp. 1268-1275). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>On Fusion of Sensor Measurements and Observation with Uncertain Timestamp for Target Tracking
2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1268-1275Conference paper, Published paper (Refereed)
Abstract [en]

We consider a target tracking problem where, in addition to the usual sensor measurements, accurate observations with uncertain timestamps are available. Such observations could, \eg, come from traces left by a target or from witnesses of an event, and have the potential in some scenarios to improve the accuracy of an estimate significantly. The Bayesian solution to the smoothing problem for one observation with uncertain timestamp is derived for a linear Gaussian state space model. The joint and marginal distributions of the states and uncertain time are derived, as well as the minimum mean squared error (MMSE) and maximum a posteriori (MAP) estimators. To attain an intuition for the problem in consideration a simple first-order example is presented and its posterior distributions and point estimators are compared and examined in some depth.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Uncertain timestamp, target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130349 (URN)000391273400168 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Projects
LINK-SIC, Scalable Kalman Filters
Funder
VINNOVASwedish Research CouncilSecurity Link
Available from: 2016-08-01 Created: 2016-08-01 Last updated: 2017-02-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1971-4295

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