Underwater Ambiguity Elimination Method Based on Co-Prime Sensor Array.

Tian Lan,Yilin Wang,Longhao Qiu

doi:10.3390/s20216058

Abstract

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice.

Highlights

Direction of arrival (DOA) estimation is an important array signal processing technique that finds broad applications in underwater passive sonar systems
This paper focuses on the ambiguity elimination problem rather than an underwater detection problem, so it is assumed that the SNR is enough to execute the processing flow
When the SNR level is sufficiently high, the method proposed in this paper presents the similar accuracy with little root-mean-square error (RMSE)

Summary

Introduction

Direction of arrival (DOA) estimation is an important array signal processing technique that finds broad applications in underwater passive sonar systems. An important goal for DOA estimation is to be able to locate closely spaced sources in the presence of considerable noise Utilizing this technique, passive sonar systems can detect, localize, and track underwater acoustic sources by monitoring with an array with spatially separated sensors. Conventional techniques for DOA estimation, such as beamforming, have been widely applied in the field of passive sonar They can only provide limited angular resolution. In realistic underwater environments, the received signals are temporally correlated as a result of multi-path arrivals. Under these conditions, the performance of subspace-based methods, such as multiple signal classification (MUSIC), will significantly degrade. How to approach the problems mentioned above has attracted much interest in recent years [1,2]

Methods

Discussion

Conclusion