A robust direction-of-arrival estimation method for impulsive noise environments - Wang, Meng; Feng, Da-Zheng; Chen, Mo-Han; Su, Ting-Ting; Zhang, Xue-Jun (Signal processing, 2023)
•A robust direction-of-arrival estimation method for impulsive noise environments. This algorithm is robust to the probability density function of impulsive noise.•A bi-iterative complex fixed-point algorithm is proposed for non-convex optimization, which exhibits good numerical stability and low computational complexity.•A robust non-linear function and the derived cost function for suppressing the effect of impulsive noise on parameter estimation in array signal processing.•The performance of the proposed algorithm is superior to that of many other robust direction-of-arrival estimation algorithms, especially when the underlying noise is extremely impulsive. The performance of conventional direction-of-arrival (DOA) estimation methods degrades greatly when there are few snapshots and the noise model is mismatched, especially in impulsive noise environments. To solve this problem, this paper proposes a robust DOA estimation method, which is robust to the probability density function of impulsive noise. First, by exploiting the low-rank decomposition of the residual fitting error matrix and the characteristics of impulsive noise, three conditions of a good cost function in impulsive noise environments are proposed, and a unified cost function framework is established. Based on this, a robust cost function is designed to suppress the contribution of samples with impulsive noise to the cost function. Then, a bi-iterative complex fixed-point algorithm (BI-CFPA) is developed for the nonconvex optimization problem of signal subspace estimation based on the proposed robust cost function. Theoretical analyses indicate that the BI-CFPA has excellent numerical stability and low computational complexity. Finally, with the estimated signal subspace, the multiple signal classification technique is employed to obtain DOA estimates. Simulation results show that the proposed DOA estimator performs better than existing methods in three typical impulsive noise environments, especially under fairly strong impulsive noise.