Greedy Algorithms for Compressed Sensing
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This article focuses on a greedy algorithm known as "Compressed Sensing," which utilizes Orthogonal Matching Pursuit (OMP) to achieve Direction of Arrival (DOA) estimation. This technique finds extensive applications in the field of array signal processing. Compressed sensing represents an emerging signal processing technology that leverages the sparse nature of signals to significantly reduce sampling requirements while maintaining high precision, thereby lowering the costs associated with signal acquisition and processing. Algorithmically, OMP operates by iteratively selecting the most correlated atoms from a measurement matrix and performing orthogonal projection to approximate sparse solutions. Key implementation steps include correlation computation, support set expansion, and least-squares estimation. As a result, compressed sensing is increasingly becoming a research hotspot in signal processing domains.
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