改进的变分稀疏贝叶斯学习离格 DOA 估计方法

Here, to improve processing speed and direction-of-arrival (DOA) estimation performance of array signals, an improved variational sparse Bayesian learning off-grid DOA estimation method was proposed. This method could utilize real value transformation to transform covariance matrix of vectorized receival signals in complex domain into real domain. Ideas of variational sparse Bayesian learning and grid evolution were combined to make a grid adaptively evolute from an initial uniform one to a non-uniform one in iteration process. Though grid update and grid fission alternating iterations, evolved grid points could gradually approach DOA of actual signal source. Simulation results showed that compared with traditional compressed sensing methods, the proposed method can reduce computational amount, improve computational speed, and have higher DOA estimation accuracy and DOA resolution; in the case of fewer snapshots and low signal-to-noise ratio, these advantages become more obvious; data processing results of on-lake tests further verify the effectiveness and engineering practicality of the proposed method.