Abstract

A coprime array of sensors can achieve degrees of freedom (DOFs) by possessing a uniform linear array segment of size in the difference coarray. However, the structure of difference coarray is sensitive to sensor failures. Once the sensor fails, the impact of failure sensors on the coarray structure may decrease the DOFs and cause direction finding failure. Therefore, the direction of arrival (DOA) estimation of coprime arrays with sensor failures is a significant but challenging topic for investigation. Driven by the need for remedial measures, an efficient detection strategy is developed to diagnose the coprime array. Furthermore, based on the difference coarray, we divide the sensor failures into two scenarios. For redundant sensor failure scenarios, the structure of difference coarray remains unchanged, and the coarray MUSIC (CO-MUSIC) algorithm is applied for DOA estimation. For non-redundant sensor failure scenarios, the consecutive lags of the difference coarray will contain holes, which hinder the application of CO-MUSIC. We employ Singular Value Thresholding (SVT) algorithm to fill the holes with covariance matrix reconstruction. Specifically, the covariance matrix is reconstructed into a matrix with zero elements, and the SVT algorithm is employed to perform matrix completion, thereby filling the holes. Finally, we employ root-MUSIC for DOA estimation. Simulation results verify the effectiveness of the proposed methods.

Highlights

  • The direction of arrival (DOA) estimation problem plays an important role in array signal processing

  • To perform DOA estimation for coprime array under sensor failures, we divide the sensor failures into two scenarios: redundant sensor failures and non-redundant sensor failures

  • In [21], the authors reconstruct a virtual array based on the Khatri-Rao product to handle failure array data, but this method only performs the DOA estimation of the redundant sensor failure scenario of the uniform linear array (ULA)

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Summary

Introduction

The direction of arrival (DOA) estimation problem plays an important role in array signal processing. The impact of failure sensors on the difference coarray may decrease the degrees of freedom (DOFs) and cause performance degradation In this scenario, it is important to quickly diagnose the coprime array and take remedial measures [6,7,8,9]. The covariance matrix reconstruction method based on the difference coarray proposed in [20] uses lags to occupy failed sensors This method is only applicable to the failure sensors in special positions and is not universal. In [21], the authors reconstruct a virtual array based on the Khatri-Rao product to handle failure array data, but this method only performs the DOA estimation of the redundant sensor failure scenario of the ULA.

Coprime
Failure
Redundant Sensor Failures
Non-Redundant Sensor Failures
Spatial Spectrum
Normalized
RMSE Versus SNR and Number of Snapshots versus SNR and of
Angular Resolution
Conclusions
Full Text
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