低信噪比下重构协方差矩阵的高分辨MUSIC算法

在满足对称分布的海洋噪声场中，为提高低信噪比条件下目标方位估计性能，提出一种重构信号协方差矩阵的MUSIC算法。利用数据协方差矩阵虚部与对称噪声无关的性质，根据协方差矩阵虚部和虚部MUSIC算法的预估角重构出信号协方差矩阵，在此基础上实现MUSIC算法。仿真结果表明，所提算法相比常规MUSIC算法能有效降低对称噪声的影响，提高方位估计性能，并避免双边谱的出现，有更高的分辨率和更低的分辨门限。还研究了协方差矩阵的Toeplitz修正处理对于MUSIC类算法的改善作用。仿真表明，Toeplitz修正处理能显著提高MUSIC类算法的分辨性能。

High resolution MUSIC algorithm reconstructing covariance matrix in low SNR

In the symmetrical distribution ocean noise field, in order to improve the direction-of-arrival (DOA) estima-tion performance in low signal-to-noise (SNR) conditions, a novel MUSIC algorithm that reconstructs the signal cova-riance matrix is presented. Based on the properties that the image part of data covariance matrix has no relation with the symmetric noise, the signal covariance matrix is reconstructed by using the image part of data covariance matrix and the DOA which is estimated by Image-MUSIC algorithm. Thus, the MUSIC algorithm is implemented. Simulation results show that the new algorithm effectively reduces the influence of symmetrical noise on DOA estimation and avoids bi-lateral spectrum arising, moreover the algorithm achieves higher resolution and lower resolution threshold in low SNR cases. Covariance matrix''s Toeplitz modification that aims to improve the DOA estimation performance of MUSIC algorithm has also been studied, and simulation results show that the resolution of MUSIC algorithm is improved sig-nificantly.