Improved PMHT for Multisensor-Multitarget Tracking Under Non-Gaussian Noise

In the data association of multi-target tracking, the probabilistic multi-hypothesis tracker (PMHT) algorithm has attracted the attention of many scholars because of its strong extensibility and low computational complexity. The traditional PMHT with kalman filter performs well under the Gaussian condition. However, in practical applications, the impulsive noise environment is common, in which case the performance of the PMHT becomes poor. In this paper, we apply the maximum correntropy criterion (MCC) and minimum error entropy (MEE) criterion to PMHT respectively to deal with the non-Gaussian noise. Simulation results show the effective performance of the new algorithms under multi-target and multi-sensor conditions.