Low noise background processing with a deconvolution method for the multiple signal classification azimuthal spectral estimation

The background levels of the multiple signal classification (MUSIC) algorithm for the direction of arrival (DOA) estimation are relatively high when the signal-to-noise ratio (SNR) of the receiving data is low. This paper proposes a deconvolved MUSIC (D-MUSIC) algorithm for suppressing the background levels in the DOA estimation. The D-MUSIC algorithm utilizes an analogous impulse function as the point scattering function (PSF) of the MUSIC azimuth spectrum, and then the direction of the source can be estimated by iterating the azimuth spectrum of the MUSIC algorithm based on the Richardson-Lucy (R-L) algorithm. The numerical result shows that the D-MUSIC algorithm inherits the high resolution performance of the MUSIC algorithm and manifests a lower background levels titan the MUSIC algorithm. The low background levels performance of the D-MUSIC algorithm is also verified by the data collected by a horizontal linear array during an experiment in the South China Sea.