A new measurement association mapping strategy for DOA tracking

Abstract

To solve the problem of the time-varying direction of arrival (DOA) in uniform linear array (ULA), a novel DOA tracking method based on generalized label multi-Bernoulli (GLMB) filter is proposed. Since the measurement value is a superimposed data, which will lead to the mismatch of measurement association mapping (MAP) in the GLMB filter updated step. To solve this issue, we propose a new measurement association mapping (NMAP) strategy, which redefines the MAP between the target and the measurement. Furthermore, particle filter is given to approximate the posterior distribution of the GLMB filter, and the likelihood function is replaced by the multi-signal classification (MUSIC) spatial spectrum function. Finally, by exponential weighting of the likelihood function, the number of particles in the high likelihood region increases, making the pruning and merging of the GLMB filter more effective. Compared with the existing methods, the proposed method is superior to other algorithms in tracking the source's state and estimating the number of sources.