Tracking multiple targets in cluttered environments with a probabilistic multihypothesis tracker

Details Tracking multiple targets in cluttered environments with a probabilistic multihypothesis tracker Tracking multiple targets in cluttered environments with a probabilistic multihypothesis tracker

Jun 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3086..284d&link_type=abstract

Proc. SPIE Vol. 3086, p. 284-295, Acquisition, Tracking, and Pointing XI, Michael K. Masten; Larry A. Stockum; Eds.

Mathematics

Probability

Scientific paper

Tracking multiple targets in a cluttered environment is extremely difficult. Traditional approaches use simple techniques to determine what are the true measurements by a combination of gating and some form of a nearest neighbor association. As clutter densities increase, these traditional algorithms fail to perform well. To counter this problem, the multi-hypothesis tracking (MHT) algorithm was developed. This approach enumerates almost every conceivable possible combination of measurements to determine the most likely. This process quickly becomes very complex and requires vast amounts of memory in order to store all of the possible tracks. To avoid this complexity, more sophisticated single hypothesis data association techniques have been developed, such as the probabilistic data association filter (PDAF). These algorithms have enjoyed some success but do not take advantage of any future data to help clarify ambiguous situations. On the other hand, the probabilistic multi-hypothesis tracking (PMHT) algorithm, proposed by Streit and Luginbuhl in 1995, attempts to use the best aspects of the MHT and the PDAF. In the PMHT algorithm, data is processed in batches, thereby using information from before and after each measurement to determine the likelihood of each measurement-to-track association. Furthermore, like the PDAF, it does not attempt to make hard assignments or enumerate all possible combinations. but instead associates each measurement with each track based upon its probability of association. Actual performance and initialization of the PMHT algorithm in the presence of significant clutter has not been adequately researched. This study focuses on the performance of the PMHT algorithm in dense clutter and the initialization thereof. In addition, the effectiveness of measurement attribute data is analyzed, especially as it relates to algorithm initialization. Further, it compares the performance of this algorithm to the nearest neighbor, MHT, and PDAF.

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