Tracking and data association
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
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In this paper, we present an interesting filtering algorithm to perform two major tasks, the first one consist of computing accurate estimation in jump Markov nonlinear systems, considering the case of multi-target tracking, the second one has to deal with the problem of data association problem. 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 this paper we propose to avoid the Extended Kalman filter because of its limitations and substitute it with the Unscented Kalman filter which seems to be more efficient especially according to the simulation results obtained with the nonlinear IMM algorithm (IMM-UKF) [8]. To overcome the problem of data association, we propose the use of an accelerated JPDA approach based on the depth first search (DFS) technique [10]. The derived algorithm from the combination of the IMM-UKF algorithm and the DFS-JPDA approach is noted DFS-JPDA-IMM-UKF.