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Particle Filtering Combined with Interval Methods for Tracking Applications

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

Published

Standard

Particle Filtering Combined with Interval Methods for Tracking Applications. / Gning, Amadou; Mihaylova, Lyudmila; Abdallah, Fahed; Ristic, Branko.

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/ISSNChapter (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 B-N, 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 ; Ristic, Branko. / 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 -