Abstract
A robust adaptive probability hypothesis density (PHD) filter is proposed to address the degradation of PHD performance due to an unknown process noise and measurement noise covariance matrix. Therefore, the inverse Wishart distribution is introduced to model the prior distribution of process noise and measurement noise. Meanwhile, the multi-target posterior intensity is approximated as a mixture of the inverse Wishart distribution and Gaussian distribution. The closed solution of the robust PHD filter is derived by the variational Bayes approach. Simulation results show that the proposed algorithm outperforms the Gaussian-mixed PHD filter and the variational Bayesian PHD filter in terms of target number estimation accuracy and optimal sub-pattern assignment distance.
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