Robust DOA Estimation Method for Underwater Acoustic Vector Sensor Array in Presence of Ambient Noise

Abstract

The ambient noise covariance matrix for the array of underwater acoustic vector sensors (AVSs) is not equal to a scaled identity matrix. This fact contradicts the requirement of subspace-based DOA estimation methods such as the conventional MUSIC method, leading to the performance degradation of their DOA estimation. In order to overcome this problem, we explore the real- and imaginary- property of the auto-correlation and cross-correlation of the ambient noise, and propose a MUSIC-based DOA estimation method with asymptotically Ambient Noise Elimination (Named as ANE MUSIC method). In particular, the ANE MUSIC method first transforms the array covariance matrix to a new one of which the noise is concentrated in the real part. Thus, the imaginary part of the transformed covariance matrix eliminates ambient noise. Afterwards, based on the imaginary part of the transformed covariance matrix, the ANE MUSIC method employs a real-valued Singular Value Decomposition (SVD) to complete DOA estimation. The proposed ANE MUSIC method is asymptotically independent of the ambient noise. Therefore, it is robust to the ambient noise in the case of limited snapshots. In addition, since its involved spectral searching is over only half of the total angular field-of-view with a real-valued noise subspace, the ANE MUSIC method reduces the computational complexity by about 75% in terms of spectral searching, as compared to the conventional MUSIC method which utilizes the complex-valued Eigenvalue Decomposition(EVD) and a spectral searching over the total angular field-of-view. It is noted that the proposed ANE MUSIC method does not require to know the prior noise covariance matrix, which is different from the existing prewhitening solution. Simulation results demonstrate the ANE MUSIC method performs significantly better than the other methods, especially in the case of low signal-to-noise ratios (SNRs). Moreover, it gains a certain robustness against the sensor gain-phase errors. Experimental results verify the practical effectiveness of the ANE MUSIC method, based on the real data collected by an array of two AVSs in the anechoic water tank and the real data collected by an uniformly circular array of eight AVSs in the Songhua Lake in Jilin, China.

Topics & Concepts