互耦及脉冲噪声下基于稀疏重构的鲁棒 DOA 估计算法

To deal with the problem of direction of arrival (DOA) estimation in the presence of mutual coupling and impulsive noise, an algorithm that combines M-estimate and sparse reconstruction is proposed. Firstly, a dictionary without unknown mutual coupling coefficients is obtained through identical deformation based on the Toeplitz structure of the mutual coupling matrix to eliminate the influence of mutual coupling effect. Subsequently, in order to enable the algorithm to adapt to Gaussian noise and impulsive noise of different intensities, the location score function is expressed as a linear combination of Gaussian location score function and a series of nonlinear functions, and the loss function is established with an estimate of linear combination coefficients with noise samples. Finally, the iterative hard thresholding algorithm is utilized for sparse reconstruction, and an improved strategy for signal update is employed to increase the probability of correct convergence. Simulation results show that the proposed method can effectively suppress mutual coupling effect and impulsive (Gaussian)noise, and it outperforms the existing algorithms at low signal to noise ratios and strong impulse characteristics.