9 August 2004 Probability hypothesis density filter versus multiple hypothesis tracking
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Proceedings Volume 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII; (2004); doi: 10.1117/12.543357
Event: Defense and Security, 2004, Orlando, Florida, United States
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.
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Kusha Panta, Ba-Ngu Vo, Sumeetpal Singh, Arnaud Doucet, "Probability hypothesis density filter versus multiple hypothesis tracking", Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.543357; https://doi.org/10.1117/12.543357
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KEYWORDS
Electronic filtering

Filtering (signal processing)

Digital filtering

Particles

Target detection

Nonlinear filtering

Particle filters

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