Smoothing cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking

Underwater direction-of-arrival (DOA) tracking must overcome significant challenges posed by dynamic environmental conditions that create uncertain measurement noise. This paper presents an improved cardinalized probability hypothesis density (CPHD) filter framework incorporating Sage-Husa noise estimation with backward smoothing to enhance tracking robustness. The proposed method addresses noise uncertainty through real-time variance estimation while employing smoothing techniques to refine forward filtering outputs. Experimental results demonstrate substantial improvements in both accuracy and robustness compared to conventional approaches, particularly in challenging underwater scenarios with non-stationary noise conditions.