The Maximum Likelihood-Probabilistic Data Association (MLPDA) target tracking algorithm is effective in tracking very low observable targets. A key limitation of MLPDA is that it is restricted to tracking a single target. We derive and implement a multiple target version of MLPDA called Joint MLPDA (JMLPDA). While the JMLPDA implementation presented in this paper is focused on a two-target case, this algorithm is extensible to any number of targets. The MLPDA and JMLPDA algorithms are combined to form a multi-target MLPDA tracking algorithm. Performance of the JMLPDA and the multi-target MLPDA algorithms are compared to a Probabilistic Multi-Hypothesis Tracker (PMHT) for two crossing targets, focusing on track management/update. Simulation results show that under conditions of heavy clutter, the multi-target MLPDA outperforms PMHT in terms of reduced track errors and longer track life.
Multiple Target Tracking Using Maximum Likelihood Probabilistic Data Association
01.03.2007
13273963 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Passive Target Tracking Using Maximum Likelihood Estimation
Online Contents | 1996
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Online Contents | 1998
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