摘要
In order to solve the problem of DOA （direction of arrival） estimation of underwater remote targets, a novel subspace-decomposition method based on the cross covariance matrix of the pressure and the particle velocity of acoustic vector sensor arrays （AVSA） was proposed. Whereafter, using spatio-temporal virtual tapped-delay-line, a new eigenvector-based criteria of detection of number of sources and of subspace partition is also presented. The theoretical analysis shows that the new source detection and direction finding method is different from existing AVSA based DOA estimation methods using particle velocity information of acoustic vector sensor （AVS） as an independent array element. It is entirely based on the combined information processing of pressure and particle velocity, has better estimation performance than existing methods in isotropic noise field. Computer simulations with data from lake trials demonstrate, the proposed method is effective and obviously outperforms existing methods in resolution and accuracy in the case of low signal-to-noise ratio （SNR）.
In order to solve the problem of DOA （direction of arrival） estimation of underwater remote targets, a novel subspace-decomposition method based on the cross covariance matrix of the pressure and the particle velocity of acoustic vector sensor arrays （AVSA） was proposed. Whereafter, using spatio-temporal virtual tapped-delay-line, a new eigenvector-based criteria of detection of number of sources and of subspace partition is also presented. The theoretical analysis shows that the new source detection and direction finding method is different from existing AVSA based DOA estimation methods using particle velocity information of acoustic vector sensor （AVS） as an independent array element. It is entirely based on the combined information processing of pressure and particle velocity, has better estimation performance than existing methods in isotropic noise field. Computer simulations with data from lake trials demonstrate, the proposed method is effective and obviously outperforms existing methods in resolution and accuracy in the case of low signal-to-noise ratio （SNR）.
