An improved particle filter for DIM radar target detection and tracking

Particle filtering (PF) and dynamic programming (DP) are two famous track-before-detect (TBD) algorithms which have been used widely for solving problems of dim radar target detection and tracking under low signal-to-noise ratio (SNR). A new method that combines these two algorithms, via. the name DPPF, is proposed in this paper. It uses Halton sequence for generating uniformly distributed particles in the state space. After that, weights of these particles are calculated and then the rearranged weights will be integrated scans of data as the evaluation function in DP approach. This new method diversifies the particles and decreases the computational complexity due to its avoidance of predicting and updating of particles. Experimental results from both simulated and real data verify that DPPF algorithm can improve the performance of detecting and tracking dim radar target.