Abstract
As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density (P-PHD) filter would decline when clustering algorithm is used to extract target states, a free clustering optimal P-PHD (FCO-P-PHD) filter is proposed. This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states. Besides, as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density, through decoupling process, a new single-sensor free clustering state extraction method is proposed. By combining this method with standard P-PHD filter, FC-P-PHD filter can be obtained, which significantly improves the tracking performance of P-PHD filter. In the end, the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
Published Version
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