A Fast JPDA-IMM-PF based DFS Algorithm for Tracking Highly Maneuvering Targets

摘要

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF). Even if the later algorithm proved its efficacy in nonlinear model case; it presents a serious drawback in case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Fitter (PF). To overcome the problem of data association, we propose the use of an accelerated JPDA approach based on the depth first search (DFS) technique. The derived algorithm from the combination of the IMM-PF algorithm and the DFS-JPDA approach is noted DFS-JPDA-IMM-PF.