Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation
Signal Processing
2019-06-04 v2 Information Retrieval
Optimization and Control
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.
Keywords
Cite
@article{arxiv.1903.07158,
title = {Joint Block Low Rank and Sparse Matrix Recovery in Array Self-Calibration Off-Grid DoA Estimation},
author = {Cheng-Yu Hung and Mostafa Kaveh},
journal= {arXiv preprint arXiv:1903.07158},
year = {2019}
}