Abstract:

The spatial spectrum estimation is a hot issue of underwater acoustic array signal processing research. It is generally believed that the information of target and background interference is contained in the covariance matrix of received signal, and thus most of the existing spectral estimation techniques study the property of the covariance matrix of the received signal. Because of the correlation between signal and noise, it will maximize the noise component of the covariance matrix primary diagonal elements. Aiming at this feature, the underwater acoustic array sampling matrix inversion (SMI) minimum variance distortionless response (MVDR) beamforming technique is proposed based on diagonal reduction, on how diagonal reduction affects output signal is deduced to noise ratio in the output power spectrum target direction from a theoretical perspective, and the selective principle of the optimal diagonal reduction coefficients is analyzed. And according to the actual engineering application, how to obtain the optimal diagonal reduction coefficients from the finite snapshot estimation sampling matrix is studied. This method does not need to estimate the number of signal sources. By algorithm simulation and sea trial data processing, through comparisons, based on diagonal reduction, validates the validity and reliability of SMIMVDR spatial spectral estimation. In the underwater acoustic array signal processing environment whose background noise level is strong, the proposed method can effectively improve the ability of the sonar multitarget resolution under Gauss white noise background.