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Proceedings Paper

Probability hypothesis density filter versus multiple hypothesis tracking
Author(s): Kusha Panta; Ba-Ngu Vo; Sumeetpal Singh; Arnaud Doucet
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Paper Abstract

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. It propagates only the first order moment instead of the full multi-target posterior. Recently, a sequential Monte Carlo (SMC) implementation of PHD filter has been used in multi-target filtering with promising results. In this paper, we will compare the performance of the PHD filter with that of the multiple hypothesis tracking (MHT) that has been widely used in multi-target filtering over the past decades. The Wasserstein distance is used as a measure of the multi-target miss distance in these comparisons. Furthermore, since the PHD filter does not produce target tracks, for comparison purposes, we investigated ways of integrating the data-association functionality into the PHD filter. This has lead us to devise methods for integrating the PHD filter and the MHT filter for target tracking which exploits the advantage of both approaches.

Paper Details

Date Published: 9 August 2004
PDF: 12 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.543357
Show Author Affiliations
Kusha Panta, Univ. of Melbourne (Australia)
Ba-Ngu Vo, Univ. of Melbourne (Australia)
Sumeetpal Singh, Univ. of Melbourne (Australia)
Arnaud Doucet, Univ. of Cambridge (United Kingdom)

Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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