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Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation

2019/03/17 by Cheng‐Yu Hung, M. Kaveh, Hung, Cheng-Yu +1
Computer Science · Engineering · #Direction-of-Arrival Estimation Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Information Retrieval (cs.IR) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #Structural Health Monitoring Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1903.07158

openalex publication_date 2019/03/17 · openalex created_date 2019/03/22 · openalex updated_date 2026/07/28

Abstract

This letter addresses the estimation of directions-of-arrival (DoA) by a sensor array using a sparse model in the presence of array calibration errors and off-grid directions. The received signal utilizes previously used models for unknown errors in calibration and structured linear representation of the off-grid effect. A convex optimization problem is formulated with an objective function to promote two-layer joint block-sparsity with its second-order cone programming (SOCP) representation. The performance of the proposed method is demonstrated by numerical simulations and compared with the Cramer-Rao Bound (CRB), and several previously proposed methods.

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