Low-rank rotation matrices-based DOA estimation with a moored circular vector sensor array

Abstract

Water currents affect circular vector sensor arrays (CVSAs) suspended from a moored platform, causing them to rotate underwater. This rotation alters the direction of sources within the array coordinate system over time. Traditional methods that rely on numerous snapshots often yield inaccurate results, particularly for faint sources. To improve the accuracy, we introduce a direction-finding technique for CVSAs that employs low-rank rotation matrices. The low-rank rotation matrices are constructed using the heading information of the CVSAs and the subregion array manifold vector matrices to achieve spatial focusing. When these matrices are applied to the measurement data, the resulting covariance matrix displays subspace characteristics similar to that of a stationary CVSA. Our performance analysis revealed that low-rank rotation matrices offer higher focusing gains than conventional rotation matrices. The method proposed in this study effectively improves the direction estimation performance for weak targets and extends the practical applicability of measurement techniques for rotating platforms. Both simulations and experiments confirm that our approach outperforms the modified traditional beamforming and other spatial focusing techniques in terms of resolution and precision. Notably, when the signal-to-noise ratio (SNR) is below -4 dB, the resolution for distinguishing between the two sources increases by more than 50%.