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Particle filtering for positioning and tracking applications
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions.

In many practical applications, signal quantization and algorithmic complexity are fundamental issues. For measurement quantization, estimation performance is analyzed in detail. The algorithmic complexity is addressed for the marginalized particle filter, where the Kalman filter solves a linear subsystem subject to Gaussian noise efficiently.

The particle filter is adopted to several positioning and tracking applications and compared to traditional approaches. Particularly, the use of external database information to enhance estimation performance is discussed. In parallel, fundamental limits are derived analytically or numerically using the Cramér-Rao lower bound, and the result from estimation studies is compared to the corresponding lower bound. A framework for map-aided positioning at sea is developed, featuring an underwater positioning system using depth information and readings from a sonar sensor and a novel surface navigation system using radar measurements and sea chart information. Bayesian estimation techniques are also used to improve position accuracy for an industrial robot. The bearings-only tracking problem is addressed using Bayesian techniques and map information is used to improve the estimation performance. For multiple-target tracking problems data association is an important issue. A method to incorporate classical association methods when the estimation is based on the particle filter is presented. A real-time implementation of the particle filter as well as hypothesis testing is introduced for a collision avoidance application.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University Electronic Press, 2005. , 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 924
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-29608Local ID: 14987ISBN: 91-85297-34-8 (print)OAI: oai:DiVA.org:liu-29608DiVA: diva2:250425
Public defence
2005-03-18, Sal Visionen, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-11-29Bibliographically approved
List of papers
1. Filtering and Estimation for Quantized Sensor Information
Open this publication in new window or tab >>Filtering and Estimation for Quantized Sensor Information
2005 (English)Report (Other academic)
Abstract [en]

The implication of quantized sensor information on estimation and filtering problems is studied. The close relation between sampling and quantization theory was earlier reported by Widrow, Kollar and Liu (1996). They proved that perfect reconstruction of the probability density function (pdf) is possible if the characteristic function of the sensor noise pdf is band-limited. These relations are here extended by providing a class of band-limited pdfs, and it is shown that adding such dithering noise is similar to anti-alias filtering in sampling theory. This is followed up by the implications for Maximum Likelihood and Bayesian estimation. The Cramer-Rao lower bound (CRLB) is derivedfor estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter approaches can perform quite bad.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 14 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2674
Keyword
Quantization, Estimation, Filtering, Cramér-Rao lower bound
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56024 (URN)LiTH-ISY-R-2674 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-12Bibliographically approved
2. Complexity Analysis of the Marginalized Particle Filter
Open this publication in new window or tab >>Complexity Analysis of the Marginalized Particle Filter
2005 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 11, 4408-4411 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, the computational complexity of the marginalized particle filter is analyzed and a general method to perform this analysis is given. The key is the introduction of the equivalent flop measure. In an extensive Monte Carlo simulation, different computational aspects are studied and compared with the derived theoretical results.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2005
Keyword
Complexity analysis, Kalman filter, Equivalent flop, Marginalized particle filter, Nonlinear estimation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-11751 (URN)10.1109/TSP.2005.857061 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2013-07-17
3. Bayesian Surface and Underwater Navigation
Open this publication in new window or tab >>Bayesian Surface and Underwater Navigation
2006 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 11, 4204-4213 p.Article in journal (Refereed) Published
Abstract [en]

A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.

Keyword
Cramér-Rao lower bound, Particle filter, Recursive Bayesian estimation, Sea navigation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-50088 (URN)10.1109/TSP.2006.881176 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12
4. Bayesian State Estimation of a Flexible Industrial Robot
Open this publication in new window or tab >>Bayesian State Estimation of a Flexible Industrial Robot
2005 (English)Report (Other academic)
Abstract [en]

A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 10 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2677
Keyword
Industrial robot, Positioning, Estimation, Particle filter, Extended Kalman filter, Cramér–Rao lower bound
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56016 (URN)LiTH-ISY-R-2677 (ISRN)
Projects
Vinnova Excellence Center LINK-SICSSF project Collaborative Localization
Funder
VinnovaSwedish Foundation for Strategic Research
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-13Bibliographically approved
5. Recursive Bayesian Estimation: Bearings-Only Applications
Open this publication in new window or tab >>Recursive Bayesian Estimation: Bearings-Only Applications
2005 (English)In: IEE Proceedings - Radar Sonar and Navigation, ISSN 1350-2395, E-ISSN 1359-7086, Vol. 152, no 5, 305-313 p.Article in journal (Refereed) Published
Abstract [en]

Recursive Bayesian estimation methods are applied to several angle-only applications. Air-to-air passive ranging, in addition to an air-to-sea application with terrain induced constraints, is discussed. The incorporation of terrain information improves estimation performance. The bearings-only problem is also discussed using experimental data from a torpedo, i.e. sea-to-sea with a passive sonar sensor. The Bayesian estimation problem is solved using the particle filter and the marginalised particle filter. For comparison, a filter bank method using range parameterised extended Kalman filters is used.

Keyword
Bayes methods, Kalman filters, Passive filters, Recursive estimation, Sonar signal processing, Target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-29601 (URN)10.1049/ip-rsn:20045073 (DOI)14979 (Local ID)14979 (Archive number)14979 (OAI)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13
6. Monte Carlo Data Association for Multiple Target Tracking
Open this publication in new window or tab >>Monte Carlo Data Association for Multiple Target Tracking
2001 (English)In: Proceedings of the 2001 IEE International Seminar on Target Tracking: Algorithms and Applications, 2001, 13/1-13/5 p.Conference paper, Published paper (Refereed)
Abstract [en]

The data association problem occurs in multiple target tracking applications. Since nonlinear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step is crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and joint probabilistic data association (JPDA) hypothesis calculations. A comparison with classical EKF based data association methods such as the nearest neighbor (NN) method and the JPDA method is made. The NN association method is also applied to the particle filter method. Multiple target tracking using particle filtering increases the computational burden, therefore a control structure for the number of samples needed is proposed. A radar target tracking application is used in a simulation study for evaluation.

Keyword
Bayes method, Kalman filter, Nonlinear estimation, Radar tracking
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-29618 (URN)10.1049/ic:20010239 (DOI)14997 (Local ID)14997 (Archive number)14997 (OAI)
Conference
IEE International Seminar on Target Tracking: Algorithms and Applications, Enschede, The Netherlands, October, 2001
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-03-29
7. Auxiliary Particle Filters for Tracking a Maneuvering Target
Open this publication in new window or tab >>Auxiliary Particle Filters for Tracking a Maneuvering Target
2000 (English)In: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, 3891-3895 vol.4 p.Conference paper, Published paper (Refereed)
Abstract [en]

We consider the recursive state estimation of a highly maneuverable target. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the maneuvering target tracking problem. These Monte Carlo filters perform optimal inference by simulating a large number of tracks, or particles. Each particle is assigned a probability weight determined by its likelihood. The maina dvantage of our approach is that linearizations and Gaussian assumptions need not be considered. Instead, a nonlinear model is directly used during the prediction and likelihood update. Detailed nonlinear dynamics models and non-Gaussian sensors can therefore be utilized in an optimal manner resulting in high performance gains. In a simulation comparison with current state-of-the-art tracking algorithms we show that our approach yields performance improvements. Moreover, incorporation of physical constraints with sustained optimal performance is straight forward, which is virtually impossible to incorporate for linear Gaussian filters. With the particle filtering approach we advocate these constraints are easily introduced and improve the results.

Place, publisher, year, edition, pages
IEEE, 2000
Keyword
Bayes methods, Filtering theory, Probability, State estimation, Target tracking
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-29619 (URN)10.1109/CDC.2000.912320 (DOI)14998 (Local ID)0-7803-6638-7 (ISBN)14998 (Archive number)14998 (OAI)
Conference
39th IEEE Conference on Decision and Control, Sydney, Australia, 12-15 December, 2000
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2014-12-17
8. Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application
Open this publication in new window or tab >>Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application
2004 (English)In: Proceedings of the 2004 American Control Conference, 2004, 3435-3440 p.Conference paper, Published paper (Refereed)
Abstract [en]

A growing research topic within the automotive industry is active safety systems. These systems aim at helping the driver avoid or mitigate the consequences of an accident. In this paper a collision mitigation system that performs late braking is discussed. The brake decision is based on estimates from tracking sensors. We use a Bayesian approach, implementing an extended Kalman filter (EKF) and a particle filter to solve the tracking problem. The two filters are compared for different sensor noise distributions in a Monte Carlo simulation study. In particular a bi-modal Gaussian distribution is proposed to model measurement noise for normal driving. For ideal test conditions the noise probability density is derived from experimental data. The brake decision is based on a statistical hypothesis test, where collision risk is measured in terms of required acceleration to avoid collision. The particle filter method handles this test easily. Since the test is not analytically solvable a stochastic integration is performed for the EKF method. Both systems perform well in the simulation study under the assumed sensor accuracy. The particle filter based algorithm is also implemented in a real-time testbed and fulfilled the on-line requirements.

Keyword
Bayes methods, Gaussian distribution, Kalman filters, Monte Carlo methods, Automobile industry, braking, Collision avoidance, Decision making, Road safety, Safety systems, Statistical testing, Stochastic processes, Tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-24139 (URN)3722 (Local ID)0-7803-8335-4 (ISBN)3722 (Archive number)3722 (OAI)
Conference
2004 American Control Conference, Boston, MA, USA, June-July, 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-08-29

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  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Output format
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  • asciidoc
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