Due to the difficulty in association and estimation of multi-target track in the presence of data association uncertainty

clutter

noise and miss-detection

a fuzzy clustering based algorithm for track continuity in probability hypothesis density (PHD) filter 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 with the maximum entropy fuzzy clustering.Finally

the tracks are maintained by the use of all the information.Different from the traditional estimate-to-track association

the proposed algorithm does not update the track information by simply summing the log likelihood ratios between adjacent frames

but takes the entire multi-frame 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.