Robust Probability Hypothesis Density Filtering: Theory and Algorithms

Abstract

Multi-target tracking (MTT) serves as a cornerstone technology in information fusion, yet faces significant challenges in robustness and efficiency when dealing with model uncertainties, clutter interference, and target interactions. Conventional approaches like Gaussian Mixture PHD (GM-PHD) and Cardinalized PHD (CPHD) filters suffer from inherent limitations including combinatorial explosion, sensitivity to birth/death process parameters, and numerical instability. This study proposes an innovative minimax robust PHD filtering framework with four key contributions: (1) A theoretically derived robust GM-PHD recursion algorithm that achieves optimal worst-case error control under bounded uncertainties; (2) An adaptive real-time parameter adjustment mechanism ensuring stability and error bounds; (3) A generalized heavy-tailed measurement likelihood function maintaining polynomial computational complexity; (4) A novel partition-based credibility weighting method for extended targets. The research not only establishes rigorous convergence guarantees and proves the uniqueness of PHD solutions, but also verifies algorithmic equivalence with standard GM-PHD. Experimental results demonstrate that in high-clutter environments, this method achieves a remarkable 32.4% reduction in OSPA error and 25.3% lower cardinality RMSE compared to existing techniques, while maintaining real-time processing capability at 15.3 milliseconds per step. This breakthrough lays a crucial foundation for reliable MTT in safety-critical applications.