A robust l_1 penalized DOA estimator

TL;DR

This paper introduces gridless, low-complexity LASSO-based methods for DOA estimation that perform comparably to maximum likelihood estimators but with reduced sensitivity to source power variations.

Contribution

It presents novel gridless LASSO techniques for DOA estimation that overcome previous grid size limitations and reduce computational complexity.

Findings

Performance close to ML estimator in simulations

Less sensitive to source power differences

Reduced computational complexity

Abstract

The SPS-LASSO has recently been introduced as a solution to the problem of regularization parameter selection in the complex-valued LASSO problem. Still, the dependence on the grid size and the polynomial time of performing convex optimization technique in each iteration, in addition to the deficiencies in the low noise regime, confines its performance for Direction of Arrival (DOA) estimation. This work presents methods to apply LASSO without grid size limitation and with less complexity. As we show by simulations, the proposed methods loose a negligible performance compared to the Maximum Likelihood (ML) estimator, which needs a combinatorial search We also show by simulations that compared to practical implementations of ML, the proposed techniques are less sensitive to the source power difference.