解卷积的多重信号分类算法方位谱低背景处理方法

针对信噪比较低时,多重信号分类(Multiple Signal Classification,MUSIC)算法方位谱背景级较高的问题,提出了一种解卷积的MUSIC方位估计算法(Deconvolvecd MUSIC,D-MUSIC)。该方法用一个类似冲激函数作为MUSIC算法输出方位谱的点散射函数(Point Scattering Function,PSF),然后基于解卷积图像复原理论,利用该点散射函数和RichardsonLucy(R-L)迭代算法对MUSIC算法的方位谱进行解卷积,获得D-MUSIC算法的方位谱,达到降低方位谱背景级的目的。仿真表明,该方法继承了MUSIC算法的高分辨性能,且可以明显降低方位谱的背景级,具有较好的方位估计性能。对南海海上试验的水平阵数据进行处理,分析比较了利用MUSIC算法和解卷积MUSIC算法获得的方位谱时间历程图,分析结果有效验证了D-MUSIC算法性能的优越性。 

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.