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Particle Filtering Combined with Interval Methods for Tracking Applications

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

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Particle Filtering Combined with Interval Methods for Tracking Applications. / Gning, Amadou; Mihaylova, Lyudmila; Abdallah, Fahed et al.
Integrated Tracking, Classification, and Sensor Management: Theory and Applications. ed. / Mahendra Mallick; Vikram Krishnamurthy; Ba-Ngu Vo. New Jersey: John Wiley and Sons, 2012. p. 43-74.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

Harvard

Gning, A, Mihaylova, L, Abdallah, F & Ristic, B 2012, Particle Filtering Combined with Interval Methods for Tracking Applications. in M Mallick, V Krishnamurthy & B-N Vo (eds), Integrated Tracking, Classification, and Sensor Management: Theory and Applications. John Wiley and Sons, New Jersey, pp. 43-74. <http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470639059.html>

APA

Gning, A., Mihaylova, L., Abdallah, F., & Ristic, B. (2012). Particle Filtering Combined with Interval Methods for Tracking Applications. In M. Mallick, V. Krishnamurthy, & B.-N. Vo (Eds.), Integrated Tracking, Classification, and Sensor Management: Theory and Applications (pp. 43-74). John Wiley and Sons. http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470639059.html

Vancouver

Gning A, Mihaylova L, Abdallah F, Ristic B. Particle Filtering Combined with Interval Methods for Tracking Applications. In Mallick M, Krishnamurthy V, Vo BN, editors, Integrated Tracking, Classification, and Sensor Management: Theory and Applications. New Jersey: John Wiley and Sons. 2012. p. 43-74

Author

Gning, Amadou ; Mihaylova, Lyudmila ; Abdallah, Fahed et al. / Particle Filtering Combined with Interval Methods for Tracking Applications. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. editor / Mahendra Mallick ; Vikram Krishnamurthy ; Ba-Ngu Vo. New Jersey : John Wiley and Sons, 2012. pp. 43-74

Bibtex

@inbook{f3c1611023b64e569dfcf0452ba24713,
title = "Particle Filtering Combined with Interval Methods for Tracking Applications",
abstract = "This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.",
keywords = "Sequential Bayesian Estimation, , nonlinear estimation, Box Particle filters, tracking, nonlinear filtering, interval uncertainty",
author = "Amadou Gning and Lyudmila Mihaylova and Fahed Abdallah and Branko Ristic",
year = "2012",
month = nov,
day = "13",
language = "English",
isbn = "978-0470639054",
pages = "43--74",
editor = "Mahendra Mallick and Vikram Krishnamurthy and Ba-Ngu Vo",
booktitle = "Integrated Tracking, Classification, and Sensor Management",
publisher = "John Wiley and Sons",

}

RIS

TY - CHAP

T1 - Particle Filtering Combined with Interval Methods for Tracking Applications

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

AU - Abdallah, Fahed

AU - Ristic, Branko

PY - 2012/11/13

Y1 - 2012/11/13

N2 - This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.

AB - This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.

KW - Sequential Bayesian Estimation,

KW - nonlinear estimation

KW - Box Particle filters

KW - tracking

KW - nonlinear filtering

KW - interval uncertainty

M3 - Chapter (peer-reviewed)

SN - 978-0470639054

SP - 43

EP - 74

BT - Integrated Tracking, Classification, and Sensor Management

A2 - Mallick, Mahendra

A2 - Krishnamurthy, Vikram

A2 - Vo, Ba-Ngu

PB - John Wiley and Sons

CY - New Jersey

ER -