Improved Multiple Model Particle PHD and CPHD Filters
改进的多模型粒子PHD和CPHD滤波算法

The multiple model probability hypothesis density (PHD) filter is an effective algorithm for tracking multiple maneuvering targets. However, when the conditional mode probabilities have small values, there is a particle degenerate problem and the Poisson assumption for the target number distribution will lead to an exaggerating effect of missed detections on the target number estimation. To solve these problems, an improved algorithm is proposed in this paper, which approximates the model conditional probability hypothesis density of target states by particles, and makes the interaction between survival targets by resampling, without any a priori assumption of the noise. Further more, the improved algorithm is implemented in the framework of the cardinalized PHD (CPHD) filter, so as to improve the accuracy of target number estimation. The simulation results show that the improved algorithm has better performance in terms of state filtering and target number estimation, so that this algorithm will have good application prospects.