A New Atomic Norm for DOA Estimation With Gain-Phase Errors

TL;DR

This paper introduces GP-ANM, a novel atomic norm for DOA estimation that effectively handles gain-phase errors, providing an efficient SDP-based solution with theoretical regularization parameter derivation.

Contribution

The paper proposes a new atomic norm called GP-ANM for DOA estimation with gain-phase errors, including a derived dual norm, SDP formulation, and theoretical regularization parameter.

Findings

Outperforms existing subspace and sparse methods in gain-phase error scenarios

Provides an efficient SDP-based DOA estimation method

Includes theoretical derivation of regularization parameter

Abstract

The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.