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

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 direction-of-arrival (DOA) estimation methods such as the conventional multiple signal classification (MUSIC) method, leading to the performance degradation of their DOA estimation. To overcome this problem, we explore the real and imaginary properties of the autocorrelation and cross correlation of the ambient noise and propose a MUSIC-based DOA estimation method with asymptotically ambient noise elimination (named ANE MUSIC method). In particular, the ANE MUSIC method first transforms the array covariance matrix to a new one in which the noise is concentrated in the real part. Thus, the imaginary part of the transformed covariance matrix eliminates ambient noise. Afterward, based on the imaginary part of the transformed covariance matrix, the ANE MUSIC method employs a real-valued (RV) 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 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 RV noise subspace, the ANE MUSIC method reduces the computational complexity by about 75% in terms of spectral searching, when compared to the conventional MUSIC method that 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 knowing the prior noise covariance matrix, which is different from the existing prewhitening solution. Simulation results demonstrate that 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 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 a uniformly circular array of eight AVSs in the Songhua Lake in Jilin, China.

Automated summary

In this study, a DOA estimation method named ANE MUSIC is proposed to overcome the problem of non-identity covariance matrix for underwater acoustic vector sensors (AVSs). The method eliminates ambient noise by transforming the covariance matrix and employs real-valued singular value decomposition (SVD) for DOA estimation. Compared to the conventional MUSIC method, the ANE MUSIC method does not require prior knowledge of the noise covariance matrix and achieves better performance with reduced computational complexity.