Multiple Rao-Blackwellized particle filtering for target tracking in urban environments

Phani Chavali, Arye Nehorai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose a new filtering algorithm for joint tracking of multiple target states and the channel state between each pair of antennas in a radar network. The problem of tracking multiple targets in complex scenarios, such as an urban environment, poses a computational challenge as standard particle filtering (SPF) requires large number of particles to obtain an accurate estimate of the high-dimensional state vector. In this paper, we develop a hybrid filter based on the combination of multiple particle filtering (MPF) and Rao-Blackwellized particle filtering (RBPF) by exploiting the structure in the state-space model. Numerical simulations show that the proposed multiple Rao-Blackwellized particle filtering (MRBPF) performs better than the SPF and the RBPF.

Original languageEnglish
Title of host publication2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages409-412
Number of pages4
DOIs
StatePublished - 2011
Event2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico
Duration: Dec 13 2011Dec 16 2011

Publication series

Name2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Conference

Conference2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Country/TerritoryPuerto Rico
CitySan Juan
Period12/13/1112/16/11

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