Efficient DOA Estimation Method with Ambient Noise Elimination for Array of Underwater Acoustic Vector Sensors

Abstract

The ambient noise covariance matrix for the array of underwater acoustic vector-sensors (AVSs) is not equal to an identity matrix with a constant. This fact contradicts the requirement of subspace-based DOA estimation methods such as the conventional MUSIC method, leading to the performance degradation of DOA estimation. In order to overcome this problem, we propose an efficient DOA estimation method with Ambient Noise Elimination (Named as ANE method). In particular, the ANE method first transforms the array covariance matrix to a new one of which the imaginary part eliminates ambient noises. Afterwards, based on the imaginary part of the new covariance matrix, the ANE method completes DOA estimation. The ANE method involves the real-valued Singular Value Decomposition(SVD) and thus it is computationally more efficient than the conventional MUSIC method with the complex-valued Eigenvalue Decomposition(EVD). Simulation and experimental results demonstrate the ANE method is superior to the other methods, especially in a low signal-to-noise ratio (SNR).