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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