Measurement-driven adaptive likelihood passive weak target tracking

Based on the theory of random finite sets, the track-before-detect for passive sonar is studied, and the adaptive weighted spatial spectrum of multiple signal classification (MUSIC) method is used as pseudo-likelihood ratio function to study the MUSIC-based approximate multi-Bernoulli filtering algorithm. Aiming at the problem of slow response speed of the algorithm to target regeneration, a measurement-driven target regeneration model is proposed. The simulation results show that the proposed algorithm has better tracking performance and less computation than the traditional algorithm at low SNR, and the improved model can significantly reduce the response time of the algorithm to the new target, which is improved by more than 50%. Experimental results show that the proposed method has strong robustness and can track multiple targets accurately at low SNR.