Smooth Variable Structure Filter in Random Finite Set Applications

Data association and filter robustness are two key problems for general multiple target tracking. Classic data association methods have high computational complexity, and the gold standard Kalman Filter (KF) suffers when the motion model does not match target behavior. Random Finite Set (RFS) based filters such as the Probability Hypothesis Density (PHD) filter solves the computational complexity issue of data association by using one multiple-target filter instead of multiple single-target filters in parallel, and the Smooth Variable Structure Filter (SVSF) improves filter robustness by bounding the estimation error using sliding mode control theory. However, there has not been an attempt to merge these two filtering strategies in literature. This paper presents 3 contributions to this problem: a methodology to combine the SVSF filtering strategy into the PHD filtering framework, results that show improvement in performance over traditional PHD filtering, and modifications to improve computational complexity. Three nonlinear filters are created and tested against the two common nonlinear PHD filters in a complex simulation with time varying targets, model mismatch, and high process noise. The variants show stability and robustness in situations where the original filters diverges. The filters are evaluated using the Optimal Subpattern Assignment (OSPA) metric.