Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation
This is an incremental improvement for array signal processing in applications like radar or communications, addressing specific calibration and off-grid issues.
The paper tackles direction-of-arrival estimation in sensor arrays with calibration errors and off-grid directions by formulating a convex optimization problem to promote joint block-sparsity, demonstrating performance through numerical simulations compared to the Cramer-Rao Bound and existing methods.
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.