Track Before Detect Of Stealthy Targets Algorithm Based On Particle Filter

In recent years, with the widespread application of the stealth technology,anti-jamming and anti-radiation missile technology, radar is facing a growing crisis ofsurvival. Detection and tracking for dim targets in low signal-to-noise ratio (SNR) is adifficult technical problem that the radar detection system often face and urgently to besolved in the complicated war environment, track-before-detect (TBD) is a effectivemethod to solve this problem, the research has important theoretical and applicationvalue.This paper first discusses the particle filter theory, based on the traditional particlefilter algorithm in the sample impoverishment problem, and gives an improvedregularized particle filter (RPF) algorithm. Then, builds several target motion statemodels and radar measurement model of track-before-detect algorithm based on particlefilter, and introduces the classic PF-TBD and the RPF-TBD algorithms process. Finally,sets up two experimental scene of the constant velocity linear motion and maneuvermotion, uses the PF-TBD and the RPF-TBD algorithms for the simulation experiment,and makes a comparative analysis of the detection and tracking performance for thesetwo algorithms, the experimental results show that the PF-TBD and the RPF-TBDalgorithms have a good detection and tracking performance for dim targets in low SNR,and the performance of the RPF-TBD algorithm is better than the PF-TBD algorithm.