Abstract:

Considering the lower estimated accuracy of traditional algorithms in multitarget tracking system, a Gaussian mixture probability hypothesis density (PHD) filtering algorithm based on kernel density estimation is proposed. After pruning and merging in this algorithm, the Meanshift algorithm is introduced to estimate kernel density of Gaussian mixture PHD distribution density function, which replaces the traditional state estimation methods. Finally, the estimated peak value is used as the state value. Simulation results show that compared with the traditional algorithms, the proposed algorithm has a higher tracking accuracy.