Iteratively reweighted lp norm minimization for DOD and DOA estimation in bistatic MIMO radar under impulsive noise

In this paper, an iterative-reweighting-based algorithm is proposed to improve the performance of direction of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic MIMO radar under impulsive noise. The BSUM-RELAX algorithm is generalized to its two dimension and multiple measurement vectors (MMV) version for robust DOD and DOA estimation. The optimization problem is cast to an lp (with 0 < p < 2) norm minimization problem to alleviate the impact of the impulsive noise, which is iteratively solved by minimizing the objective function based on each variable. Nevertheless, such a problem is of non-convexity and highly depends on a good guess of all the initial values of DODs and DOAs of multiple targets in order to obtain considerable performance. Therefore, an off-grid two-dimensional Newtonized orthogonal matching pursuit (2D-NOMP) algorithm is developed to obtain a near-optimal initial guess. Simulations are conducted to verify the robustness of the proposed approach for DOD and DOA estimation under impulsive noise.

Automated summary

An iterative-reweighting-based algorithm is proposed in this paper to improve the performance of DOD and DOA estimation in bistatic MIMO radar under impulsive noise. The optimization problem is cast to an lp norm minimization problem and solved iteratively by minimizing the objective function based on each variable. Additionally, an off-grid algorithm is developed to obtain a near-optimal initial guess.