Abstract
In this paper, we propose a new direction of arrival (DOA) estimation algorithm, in which DOA estimation is achieved by finding the sparsest support set of multiple measurement vectors (MMV) in an over-complete dictionary. The proposed algorithm is based on ℓp norm minimization, which belongs to non-convex optimization. Therefore, the quasi-Newton method is used to converge the iterative process. There are two advantages of this algorithm: one is the higher possibility and resolution of distinguishing closely spaced sources, and the other is the adaptive regularization parameter adjustment. Moreover, an accelerating strategy is applied in the computation, and a weighted method of the proposed algorithm is also introduced to improve the accuracy. We conducted experiments to validate the effectiveness of the proposed algorithm. The performance was compared with several popular DOA estimation algorithms and the Cramer–Rao bound (CRB).
Highlights
With extensive applications, including radar, sonar, wireless communication and remote sensing, direction of arrival (DOA) estimation has traditionally been a popular branch in the field of array signal processing [1,2]
In dividing the whole spatial domain of interests into a discrete set of potential grids, a traditional estimation module can be converted into an ill-posed inverse problem
The goal of this paper is to find a practical sparse reconstruction method that fills the missing application scenario of traditional DOA estimation algorithm
Summary
With extensive applications, including radar, sonar, wireless communication and remote sensing, DOA estimation has traditionally been a popular branch in the field of array signal processing [1,2]. As a key technology in passive radar, DOA estimation is being extensively developed to locate targets in complex electromagnetic environments without being perceived. Considering problems, such as distinguishing coherence sources and separating spatial closely sources, sparse reconstruction theory plays an important role in DOA estimation [3,4]. In dividing the whole spatial domain of interests into a discrete set of potential grids, a traditional estimation module can be converted into an ill-posed inverse problem. The MMV data module of a M sensor array takes the following form: Publisher’s Note: MDPI stays neutral
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