A fast coalescence-avoiding JPDAF

In this paper we present a new algorithm for approximating the target-measurement association probabilities of the Joint Probabilistic Data Association Filter (JPDAF). This algorithm is designed to robustify the JPDAF against track coalescence which can greatly degrade the performance of the JPDAF and other approximate algorithms. It is based on the works of Roecker and the JPDAF* of Blom and Bloem. We compare our new algorithm with the two it is based on, as well as the "cheap JPDAF" and the Set JPDAF, and show that it offers a significant improvement in computational complexity over the JPDAF*, and improvement in tracking error over the Roecker algorithm. We compare their performance with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in scenarios involving several closely-spaced targets. A consistency comparison of the various algorithms considered is also presented.