2021/11/06 by Shaoxiu Wei, Wei, Shaoxiu, Boxiang Zhang +3
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Radar Systems and Signal Processing #Signal Processing (eess.SP) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.03871
openalex publication_date 2021/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we derive the robust TPHD (R-TPHD) filter, which can adaptively learn the unknown detection profile history and clutter rate. The R-TPHD filter is derived by obtaining the best Poisson posterior density approximation over trajectories on hybrid and augmented state space by minimizing the Kullback-Leibler divergence (KLD). Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation. The Beta-Gaussian mixture model is proposed for the implementation, which is referred to as the BG-R-TPHD filter and we also propose a L-scan approximation for the BG-R-TPHD filter, which possesses lower computational burden.