Abstract:In view of the problems that traditional subspace-based algorithms have in DOA (Direction of Arrival) estimation, such as being dependent on the number of sources and having limited algorithm resolution, this paper proposes an improved Capon algorithm based on vector hydrophones (Improved Capon Estimator, ICE). This algorithm can achieve accurate DOA estimation under the condition of unknown number of sources. This method takes advantage of the characteristic that vector hydrophones can simultaneously obtain sound pressure and vibration velocity data, and constructs a vector array model. At the same time, it introduces a higher-order inverse covariance matrix within the framework of Capon-type cost functions to enhance the power of matrix inversion, suppress the interference of signal subspace components on DOA estimation, and overcome the problem that traditional subspace algorithms are dependent on prior knowledge of the number of sources. This method is based on the difference in the characteristics of the signal and noise subspaces, and regulates the high-order inverse matrix to adapt to the DOA estimation in the unknown source scenario. Numerical simulations have verified that this method can accurately achieve high-resolution DOA estimation under the condition of unknown number of sources. Compared with algorithms such as CBF and MUSIC, it has stronger resolution capability for adjacent targets and better robustness.
