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Publikasjoner (10 av 579) Visa alla publikasjoner
Forsling, R., Sjanic, Z., Gustafsson, F. & Hendeby, G. (2019). Consistent Distributed Track Fusion Under Communication Constraints. In: Proceedings of the 22nd International Conference on Information Fusion (FUSION): . Paper presented at 22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019.
Åpne denne publikasjonen i ny fane eller vindu >>Consistent Distributed Track Fusion Under Communication Constraints
2019 (engelsk)Inngår i: Proceedings of the 22nd International Conference on Information Fusion (FUSION), 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper addresses the problem of retrieving consistentestimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.

Emneord
distributed estimation, track fusion, communication constraints, covariance intersection, consistency, consistency preservation
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-159102 (URN)
Konferanse
22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019
Prosjekter
LINK-SIC
Forskningsfinansiär
Vinnova, LINK-SICSwedish Research Council, Scalable Kalman filters
Tilgjengelig fra: 2019-07-24 Laget: 2019-07-24 Sist oppdatert: 2019-09-05bibliografisk kontrollert
Fritsche, C., Orguner, U. & Gustafsson, F. (2018). Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems. In: 2018 21st International Conference on Information Fusion (FUSION): . Paper presented at 2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018 (pp. 1-8).
Åpne denne publikasjonen i ny fane eller vindu >>Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems
2018 (engelsk)Inngår i: 2018 21st International Conference on Information Fusion (FUSION), 2018, s. 1-8Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, recursive Bobrovsky-Zakai bounds for filtering, prediction and smoothing of nonlinear dynamic systems are presented. The similarities and differences to an existing Bobrovsky-Zakai bound in the literature for the filtering case are highlighted. The tightness of the derived bounds are illustrated on a simple example where a linear system with non-Gaussian measurement likelihood is considered. The proposed bounds are also compared with the performance of some well known filters/predictors/smoothers and other Bayesian bounds.

Emneord
Bayesian lower bounds, Bobrovsky-Zakai bound, filtering, prediction, smoothing, nonlinear dynamic systems
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-151671 (URN)
Konferanse
2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tilgjengelig fra: 2018-09-29 Laget: 2018-09-29 Sist oppdatert: 2018-10-22
Fritsche, C., Orguner, U., Özkan, E. & Gustafsson, F. (2018). Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering. In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018: . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018 (pp. 4289-4293). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering
2018 (engelsk)Inngår i: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, IEEE, 2018, s. 4289-4293Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, marginal versions of the Bayesian Bhattacharyya lower bound (BBLB), which is a tighter alternative to the classical Bayesian Cramer-Rao bound, for discrete-time filtering are proposed. Expressions for the second and third-order marginal BBLBs are obtained and it is shown how these can be approximately calculated using particle filtering. A simulation example shows that the proposed bounds predict the achievable performance of the filtering algorithms better.

sted, utgiver, år, opplag, sider
IEEE, 2018
Serie
IEEE International Conference on Acoustics, Speech and Signal Processing
Emneord
Performance bounds, Bayesian estimation, Bhattacharyya bounds, nonlinear filtering, particle filter
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-148405 (URN)10.1109/ICASSP.2018.8462163 (DOI)000446384604091 ()978-1-5386-4659-5 (ISBN)978-1-5386-4658-8 (ISBN)
Konferanse
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Merknad

Funding agencies: ELLIIT

Tilgjengelig fra: 2018-06-08 Laget: 2018-06-08 Sist oppdatert: 2019-06-19bibliografisk kontrollert
Zhao, Y., Fritsche, C., Yin, F., Gunnarsson, F. & Gustafsson, F. (2018). Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning. IEEE Transactions on Vehicular Technology, 67(6), 5372-5386
Åpne denne publikasjonen i ny fane eller vindu >>Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning
Vise andre…
2018 (engelsk)Inngår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, nr 6, s. 5372-5386Artikkel i tidsskrift (Fagfellevurdert) Published
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 positioning of devices based on a time series of proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Motion model will be needed together with the measurements to determine the position of the device. Therefore, sequential Monte Carlo methods, namely particle filtering and smoothing, are applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report, and is further compared to parametric Cramér-Rao lower bounds. Finally, the position accuracy is also evaluated with real experimental data.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018
Emneord
Proximity, indoor positioning, particle filtering and smoothing, Cramer-Rao lower bounds
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-147834 (URN)10.1109/TVT.2018.2799174 (DOI)000435553400053 ()2-s2.0-85041415767 (Scopus ID)
Merknad

Funding agencies: European Union FP7 Marie Curie Training Programme on Tracking in Complex Sensor Systems (TRAX) [607400]; NSFC [61701426]; Shenzhen Science and Technology Innovation Council [JCYJ20170307155957688, JCYJ20170411102101881]

Tilgjengelig fra: 2018-05-15 Laget: 2018-05-15 Sist oppdatert: 2019-02-12bibliografisk kontrollert
Fritsche, C. & Gustafsson, F. (2017). Bayesian Bhattacharyya bound for discrete-time filtering revisited. In: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 719-723).
Åpne denne publikasjonen i ny fane eller vindu >>Bayesian Bhattacharyya bound for discrete-time filtering revisited
2017 (engelsk)Inngår i: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017, s. 719-723Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed ina paper by Reece and Nicholson is revisited. It turns out that the results presented in the aforementioned contribution are incorrect, as some expectations appearing in the information matrix recursions are missing. This paper gives a generalized derivation of the N-th order Bayesian Bhattacharyya bound and presents corrected expressions for the case N = 2. A nonlinear toy example is used to illustrate the results

Emneord
Bhattacharyya bound, nonlinear filtering, mean square error inequality
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-144022 (URN)10.1109/CAMSAP.2017.8313201 (DOI)000428438100145 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Konferanse
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tilgjengelig fra: 2018-01-03 Laget: 2018-01-03 Sist oppdatert: 2018-06-11bibliografisk kontrollert
Sjanic, Z., Skoglund, M. A. & Gustafsson, F. (2017). EM-SLAM with Inertial/Visual Applications. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 273-285
Åpne denne publikasjonen i ny fane eller vindu >>EM-SLAM with Inertial/Visual Applications
2017 (engelsk)Inngår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, nr 1, s. 273-285Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017
Emneord
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-110371 (URN)10.1109/TAES.2017.2650118 (DOI)000399934000022 ()
Merknad

Funding agencies: Vinnova Industry Excellence Center LINK-SIC

Tilgjengelig fra: 2014-09-09 Laget: 2014-09-09 Sist oppdatert: 2017-05-18bibliografisk kontrollert
Braga, A. R., Fritsche, C., Bruno, M. G. S. & Gustafsson, F. (2017). Rapid System Identification for Jump Markov Non-Linear Systems. In: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 714-718). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Rapid System Identification for Jump Markov Non-Linear Systems
2017 (engelsk)Inngår i: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, 2017, s. 714-718Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This work evaluates a previously introduced algorithm called Particle-Based Rapid Incremental Smoother within the framework of state inference and parameter identification in Jump Markov Non-Linear System. It is applied to the recursive form of two well-known Maximum Likelihood based algorithms who face the common challenge of online computation of smoothed additive functionals in order to accomplish the task of model parameter estimation. This work extends our previous contributions on identification of Markovian switching systems with the goal to reduce the computational complexity. A benchmark problem is used to illustrate the results.

sted, utgiver, år, opplag, sider
IEEE, 2017
Emneord
parameter estimation, system indentification, jump Markov systems, particle filtering
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-144023 (URN)10.1109/CAMSAP.2017.8313089 (DOI)000428438100033 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Konferanse
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Prosjekter
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tilgjengelig fra: 2018-01-03 Laget: 2018-01-03 Sist oppdatert: 2018-07-06bibliografisk kontrollert
Karlsson, R. & Gustafsson, F. (2017). The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime. IEEE signal processing magazine (Print), 34(2), 60-69
Åpne denne publikasjonen i ny fane eller vindu >>The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime
2017 (engelsk)Inngår i: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, nr 2, s. 60-69Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-135786 (URN)10.1109/MSP.2016.2637418 (DOI)000397574300008 ()2-s2.0-85015356723 (Scopus ID)
Prosjekter
Wallenberg Autonomous Systems Program
Merknad

Funding agencies: Wallenberg Autonomous Systems Program

Tilgjengelig fra: 2017-03-22 Laget: 2017-03-22 Sist oppdatert: 2017-04-21bibliografisk kontrollert
Örn, D., Szilassy, M., Dil, B. & Gustafsson, F. (2016). A Novel Multi-Step Algorithm for Low-Energy Positioning Using GPS. 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. 1469-1476).
Åpne denne publikasjonen i ny fane eller vindu >>A Novel Multi-Step Algorithm for Low-Energy Positioning Using GPS
2016 (engelsk)Inngår i: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, 2016, s. 1469-1476Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

GPS is widely used for localization and tracking, however traditional GPS receivers consume too much energy for many applications. This paper implements and evaluates the performance of a low-energy GPS prototype. The main difference is that a traditional GPS needs to sample signals transmitted by satellites for 30 seconds to estimate its position. Our prototype reduces this time by three orders of magnitude and it can compute positions from only 2 milliseconds of data. We present a new algorithm that increases robustness by filtering on estimated residuals instead of using an altitude database. In addition, we show that our new algorithm works with both fixed and moving targets. The solution consists of (1) a portable device that samples the GPS signal and (2) a server that utilizes Doppler navigation and Coarse Time Navigation to estimate positions. We performed tests in a wide variety of environments and situations. These tests show that our prototype provides a median positioning error of roughly 40 meters even when the GPS receiver is moving at 80 kilometres per hour.

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-130236 (URN)000391273400195 ()9780996452748 (ISBN)
Konferanse
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Forskningsfinansiär
Swedish Institute
Tilgjengelig fra: 2016-07-22 Laget: 2016-07-22 Sist oppdatert: 2018-03-20bibliografisk kontrollert
Alickovic, E., Lunner, T. & Gustafsson, F. (2016). A System Identification Approach to Determining Listening Attention from EEG Signals. In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary (pp. 31-35). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>A System Identification Approach to Determining Listening Attention from EEG Signals
2016 (engelsk)Inngår i: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2016, s. 31-35Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.

sted, utgiver, år, opplag, sider
IEEE, 2016
Serie
European Signal Processing Conference, ISSN 2076-1465
Emneord
attention, cocktail party, linear regression (LR), finite impulse response (FIR), multivariable model, sound, EEG
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-130732 (URN)10.1109/EUSIPCO.2016.7760204 (DOI)000391891900007 ()978-0-9928-6265-7 (ISBN)978-1-5090-1891-8 (ISBN)
Konferanse
24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary
Tilgjengelig fra: 2016-08-22 Laget: 2016-08-22 Sist oppdatert: 2017-02-15
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-3270-171X