The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. To solve this problem, an improved estimate-to-track association method for track continuity is proposed in this paper. Firstly, a multi-step prediction of current target states is made, and then the weighted labels are assigned to them according to the inertia. Secondly, the fuzzy membership degrees of the current state estimates belonging to the tracks are obtained by utilizing the maximum entropy fuzzy clustering. Finally, the tracks are maintained by integrating all this information. Different from the traditional estimate-to-track association method, the proposed algorithm does not update the track information by simply summing the log likelihood ratios between adjacent scans, but take the entire multi-scan information into account by the operations such as weighting and clustering. The simulation results show that the proposed algorithm can maintain target tracks more accurately, even when the targets cross each other, implying strong robustness and excellent performance of track continuity.
A track-to-track association method for automotive perception systems
