Low Rank Matrix Recovery for Joint Array Self-Calibration and Sparse Model DoA Estimation

TL;DR

This paper introduces a novel low-rank matrix recovery method for joint array self-calibration and DoA estimation, extending single measurement models to multiple snapshots with improved computational efficiency.

Contribution

It extends self-calibration and DoA estimation to the MMV model using nuclear norm minimization and SVD for reduced complexity, enhancing joint array calibration and direction finding.

Findings

Effective in numerical simulations

Improved accuracy with multiple snapshots

Reduced computational complexity

Abstract

In this work, combined calibration and DoA estimation is approached as an extension of the formulation for the Single Measurement Vector (SMV) model of self-calibration to the Multiple Measurement Model (MMV) case. By taking advantage of multiple snapshots, a modified nuclear norm minimization problem is proposed to recover a low-rank larger dimension matrix. We also give the definition of a linear operator for the MMV model, and give its corresponding matrix representation to generate a variant of a convex optimization problem. In order to mitigate the computational complexity of the approach, singular value decomposition (SVD) is applied to reduce the problem size. The performance of the proposed methods are demonstrated by numerical simulations.