On Maximum Correntropy GM-PHD Filtering

Multi-target tracking (MTT) in real-world environments often faces the challenge of outlier measurements, which severely degrades the performance of standard MTT algorithms. This paper integrates the maximum correntropy criterion (MCC) into the Gaussian mixture probability hypothesis density (GM-PHD) filter, an implicit data association and a highly efficient MTT algorithm. The MCC provides a localized similarity measure that is inherently resilient to impulsive outliers. We embed an iterative fixed-point measurement update for the GM-PHD filter, and an adaptive kernel size design strategy is also devised. The proposed MCC-GM-PHD filter effectively suppresses the influence of large measurement residuals, while maintaining a closed-form Gaussian mixture representation. The performance of the proposed MCC-GM-PHD filter is verified via simulations.