Robust Direction-of-Arrival Estimation for Coprime Array in the Presence of Miscalibrated Sensors

Jiaxun Kou,Chunlan Jiang,Ming Li

doi:10.1109/access.2020.2971842

Abstract

Coprime arrays have been widely adopted for direction-of-arrival (DOA) estimation since it can achieve an increased number of degrees of freedom (DOF). To utilize all information received by the coprime array, array interpolation methods are developed, which construct a virtual uniform linear array (ULA) with the same aperture from the non-uniform coprime array. However, the conventional non-robust DOA estimation algorithms for coprime arrays, including the interpolation based methods, suffer from degraded performance or even failed operation when some sensors are miscalibrated. In this paper, a novel maximum correntropy criterion (MCC) based virtual array interpolation algorithm for robust DOA estimation is developed to address this problem. The proposed approach treats the miscalibrated sensor observations as outliers, and by exploiting the property of MCC, the interpolated virtual array covariance matrix is reconstructed via nuclear norm minimization (NNM) with less influence of these outliers. In this manner, the robust DOA estimation is enabled by the robustly reconstructed covariance matrix. Simulation results demonstrate that the proposed algorithm can effectively the mitigate effect of the miscalibrated sensors while maintaining the enhanced DOF offered by coprime arrays.

Highlights

Direction-of-arrival (DOA) estimation of energy-emitting sources is one of fundamental array signal processing technique which finds broad applications in radar, sonar, acoustic, navigation and wireless communication [1]–[3]
In this paper, we address the problem of estimating DOA using a coprime array with miscalibrated sensors
Without a priori knowledge of the miscalibrated sensors, the information received by them is implicitly treated as outliers, and the correntropy is introduced as the robust similarity measurement

Summary

Introduction

Direction-of-arrival (DOA) estimation of energy-emitting sources is one of fundamental array signal processing technique which finds broad applications in radar, sonar, acoustic, navigation and wireless communication [1]–[3]. On the basis of the Nyquist sampling constraint, the uniform linear array (ULA) is the mostly adopted configuration. Using ULAs, classical DOA estimation algorithms can only resolve up to M − 1 sources with M sensors [4], [5]. Sparse linear arrays, such as coprime arrays and nested arrays, received considerable attentions duo to their abilities to break through this limitation. The coprime arrays can be used to resolve O(MN ) sources with only M + N − 1 physical sensors [6]. To exploit the enhanced degree of freedom (DOF) offered by coprime array, a preprocessing procedure is taken, which derives an augmented virtual array by computing the

Objectives

Methods

Conclusion