Sparse Covariance DOA Estimation Based on Combined Information Processing of Sound Pressure and Particle Velocity

In order to make full use of the relationship between sound pressure and particle velocity in vector hydrophone, and improve the accuracy of DOA estimation, sparse covariance DOA（Direction of Arrival） estimation method based on combined information processing of pressure and particle velocity was proposed. Firstly, the correlation between sound pressure and particle vibration velocity was used to construct the array of covariance matrix. Secondly, the spatial incident angle set was divided into equal angles and the super-complete redundant dictionary was built. Then looking for the most sparse coefficient of array covariance matrix on the over-complete basis and DOA estimates were obtained by using the row numbers corresponding to the non-zero rows in the coefficient vector. Finally, the algorithm was compared with CBF algorithm and L1-SVD algorithm, simulation results show that the proposed algorithm has lower root-mean-square error（RMSE） error when the number of signal sources is 3,4,5, respectively, the method has good DOA estimation performance compared to other algorithms under the conditions of small snapshot number and low signal-to-noise ratio（SNR）.