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Proceedings Paper

Performance evaluation of nonlinear filters for tracking multiple ballistic targets
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Paper Abstract

The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. In this paper, we compare three particle filtering algorithms on a spawning ballistic target tracking scenario. One of the algorithms, the tagged particle filter (TPF), was recently developed by us. It uses separate sets of particles for separate tracks. However, data association to different tracks is interdependent. The other two algorithms implemented in this paper are the probability hypothesis density (PHD) algorithm and the joint multitarget probability density (JMPD). The PHD filter propagates the first order statistical moment of multitarget density using particles. While, the JMPD stacks the states of a number of targets to form a single particle that is representative of the whole system. Simulation results are presented to compare the performances of these algorithms.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.606493
Show Author Affiliations
A. Sinha, McMaster Univ. (Canada)
N. Nandakumaran, McMaster Univ. (Canada)
S. Sutharsan, McMaster Univ. (Canada)
T. Kirubarajan, McMaster Univ. (Canada)
A El-Fallah, Scientific Systems Co., Inc. (United States)
A. Zatezalo, Scientific Systems Co., Inc. (United States)

Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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