非均匀噪声下基于稀疏重构的声矢量传感器阵列方位估计方法

Here, to solve the problem of direction of arrival (DOA) estimation performance of acoustic vector sensor array deteriorating under non-uniform noise, sparse signal reconstruction methods based on weighted least squares (WLS) and weighted covariance matrix fitting (WCMF), respectively were proposed. Firstly, a virtual manifold matrix of acoustic vector sensor array was defined, and a covariance matrix containing sparse signal power and noise power was reconstructed. Then, in order to estimate sparse signal power and noise output power of each channel, the cost function of sparse signal power and noise power was constructed based on WLS and the sparse signal weighted minimization method. Furthermore, in order to further improve estimation accuracy of sparse signal power and noise power, the constructed cost function was improved based on WCMF criterion. Finally, the nonlinear cost function of sparse signal power and noise power was converted into a linear function by using Taylor series expansion, and the cyclic iterative algorithm was used to estimate sparse signal power and noise power. When the iteration stopped, searching power spectrum peak of sparse signal could realize DOA estimation of targets. The simulation results showed that compared with existing estimation methods under non-uniform noise, the proposed methods can improve the DOA estimation accuracy of acoustic vector sensor array under nonuniform noise.