Joint target tracking and classification via RFS-based multiple model filtering

Firstly, a multiple model extension of the random finite set (RFS)-based single-target Bayesian filtering (STBF), referred as MM-STBF, is presented to accommodate the possible target maneuvering behavior in a straightforward manner. This paper is concerned with joint target tracking and classification (JTC) which are closely coupled. In particular, we take into account extraneous target-originated measurements which were not modeled in the existing JTC algorithms. Therefore, the main contribution is that the paper derives a new JTC algorithm based on the MM-STBF, i.e., MM-STBF–JTC. The MM-STBF–JTC is an optimal Bayesian solution, which can simultaneously accommodate unknown data association, miss-detection, clutter and several measurements originated from a target. The MM-STBF–JTC can reduce to a traditional JTC algorithm under some assumptions. The simulation results are provided to demonstrate the tracking and classification performance of the MM-STBF–JTC algorithm.