Abstract:

The adaptive target birth intensity probability hypothesis density (PHD) filter is a novel measurement-driven algorithm for multi-target tracking. However, there is a normalized unbalance problem and some lags of the extracted tracks in the filter. To solve these problems, an improved algorithm is proposed. Firstly, a modified normalized factor is proposed based on the analysis of the normalized unbalance problem. Secondly, a Gaussian mixture implementation is proposed, and then a recalling procedure for track maintenance is developed, which labels each Gaussian component and recalls the previous tracks for the components with existence probabilities larger than the confirm threshold. The simulation results show that the improved algorithm has the advantages over the ordinary one in the aspects of newborn target searching and multi-target track extracting, implying good application prospect.