Block- and Rank-Sparse Recovery for Direction Finding in Partly Calibrated Arrays

Abstract

A sparse recovery approach for direction finding in partly calibrated arrays composed of subarrays with unknown displacements is introduced. The proposed method is based on mixed nuclear norm and 1 norm minimization and exploits block-sparsity and low-rank structure in the signal model. For efficient implementation a compact equivalent problem reformulation is presented. The new technique is applicable to subarrays of arbitrary topologies and grid-based sampling of the subarray manifolds. In the special case of subarrays with a common baseline our new technique admits extension to a gridless implementation. As shown by simulations, our new block- and rank-sparse direction finding technique for partly calibrated arrays outperforms the state of the art method RARE in difficult scenarios of low sample numbers, low signal-to-noise ratio or correlated signals.

References in corpus (1)

• Distributed Compressed Sensing off the Grid

Cited by in corpus (4)

• Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation
• Interference Exploitation-based Hybrid Precoding with Robustness Against Phase Errors
• A Parallel Best-Response Algorithm with Exact Line Search for Nonconvex Sparsity-Regularized Rank Minimization
• Direct Localization of Multiple Sources by Partly Calibrated Arrays