Particle filter track-before-detect algorithm with Lamarckian inheritance for improved dim target tracking

Abstract

Particle filter track-before-detect (PF-TBD) algorithms offer improvements over track-after-detect algorithms in detecting and tracking dim targets. However, it suffers from the particle collapsing problem, which can lead to deteriorated detection and tracking performance. To address this issue, a Lamarckian particle filter track-before-detect (LPF-TBD) algorithm is developed in this paper. In the LPF-TBD, before a TBD resampling process, a particle update strategy is applied, which is based on Lamarckian overriding and elitist operators designed to improve the particle diversity and efficiency. The effectiveness of the LPF-TBD algorithm is demonstrated using a widely adopted experiment on a target with a low signal-to-noise ratio in an image sequence. Compared with the currently-popular multinomial resampling PF-TBD method, the posterior distribution in the LPF-TBD can be more sufficiently approximated by the particles. Test results show that the LPF-TBD offers higher detection and tracking performance, while strengthening the algorithmic efficiency of particle filtering and evolutionary algorithms.