Probability Hypothesis Density Filter Based on Cubature Rule and Its Application to Multi-Target Tracking

In order to balance the requirement for precision and real-time estimation performance when dealing with the problem of multi-target tracking in probability hypothesis density filter algorithm, an implementation method of PHD filter based on the cubature rule was proposed. In the framework of the probability hypothesis density for the Gaussian mixture particle, the new algorithm directly used cubature rule based numerical integration method to calculate the mean and covariance of the nonlinear random function by a set of the certain particles and their weights, thereby generating the importance density function of the particle filter algorithm to achieve high-precision particle reconstruction, and approximating to the target state and probability distribution of the target number. Finally the prediction and update distributions for the new algorithm were approached in the framework of the probability hypothesis density for the Gaussian mixture. Simulation results show the effectiveness of the proposed algorithm, that Wasserstein depresses 17.32% and mean of object number advances 23.72%.