A Dynamic DOA Tracking Method for Coherent Signal Sources Based on Cat Swarm Algorithm
Jinman Li, Ming Diao, Zengmao Chen, Yongzhen Bai · 2020
Aiming at the time-varying incident angle of the coherent signal source, this paper studies the effect of updating the data covariance matrix and the value of the forgetting factor on the tracking result. On the basis of this, the idea of group intelligent search using the cat swarm optimization (CSO) algorithm is used to calculate the maximum likelihood (ML) algorithm. The huge amount of problems is improved, and a cat swarm optimization-maximum likelihood (CSO-ML) algorithm dynamic DOA tracking method is proposed. This method does not need to pre-process the sample covariance matrix using decoherence technology, and can directly process the coherent signal source, and it is still effective in the case that the algorithm such as projection approximation subspace tracking (Past) algorithm with a small forgetting factor value fails. Simulation experiments show that this method has better tracking accuracy under the condition of low signal-to-noise ratio (SNR) and small snapshots and can directly deal with coherent signal sources.