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The existing Probability Hypothesis Density (PHD) filters with birth intensity estimation only operate on single or two consecutive scan data for multi-target tracking. However, for those targets with low detection probability, it is hard to achieve a satisfactory level of track initiation and maintenance. To overcome the weakness above, we propose a modified PHD filter with adaptive birth intensity estimation. The core of the proposed filter is to define two state sets as the formal set and the temporary set. In the framework of measurement driven estimation, we classify the measurements into three categories depending on whether it is in the neighborhood of the state in above two sets. And the birth states of the formal set and the temporary set are generated by the classified measurements respectively. In addition, if there is no matching measurement for the state in the formal set, duplicate the corresponding state as the birth state of the temporary set. For each state in temporary set, we introduce a forgetting factor and a dynamic detection probability in filter to cope with the rapid decrease of its intensity due to the absence of measurement. If its forgetting factor is over the dead threshold, the state will be deleted from the set. Based on the principles above, we derive the Gaussian-mixture (GM) implementation of the PHD filter proposed in this paper. Experiment results show that, in low detection probability scenario, the modified PHD filter outperforms other PHD filters with birth intensity estimation.

The Modified Probability Hypothesis Density Filter With Adaptive Birth Intensity Estimation for Multi-Target Tracking in Low Detection Probability