Sparse Gauss-Hermite PHD Maneuvering Multi-target Tracking Algorithm

Considering the low accuracy, filter divergence, incorrect estimation of number, and other problems of nonlinear multi-target tracking based on probability hypothesis density (PHD), a sparse Gauss-Hermite PHD algorithm based on interactive multiple models is proposed. In the proposed algorithm, a sparse Gauss-Hermite integration method is adopted for prediction and measurement update, and a sparse Gauss-Hermite PHD filter is constructed. On this basis, the motion pattern uncertainty in the target maneuvering system is solved by integrating the interactive multi-model algorithm into the sparse Gauss-Hermite PHD filtering framework. The simulation results show that the proposed algorithm has a high precision, and it is accurate in estimating the number of targets.