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