A Multi-Target Tracking Formulation of SVSF With the Joint Probabilistic Data Association Technique

Abstract

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).