一种奇异值分解与子空间加权联合的改进MUSIC算法
An improved MUSIC algorithm based on singular value decomposition and subspace weighting
石依山 1尚尚 1乔铁柱 1刘强 1祝健1
作者信息
- 1. 江苏科技大学海洋学院,江苏 镇江 212003
- 折叠
摘要
在低信噪比、小快拍数等非理想条件下,经典DOA估计算法对邻近目标的分辨率严重下降,甚至失去分辨能力.针对这一问题,提出了一种将重构的接收信号协方差矩阵进行奇异值分解并与改进的加权子空间方法相结合的改进算法.该算法充分利用互相关信息构建新的接收信号协方差矩阵,并对噪声子空间信息采用新的校正方法,对噪声特征值进行改造,之后对噪声子空间进行加权,最后与信号子空间加权技术相联合.仿真实验证明,改进算法在低信噪比和小快拍数条件下可以分辨间隔4°的相邻目标,统计分析表明该算法的分辨率明显优于经典MUSIC算法.
Abstract
Under non-ideal conditions such as low signal-to-noise ratio and small number of snapshots,the classical DOA estimation algorithm seriously degrades the resolution of adjacent targets and even loses the resolu-tion.To solve this problem,an improved algorithm is proposed that combines the singular value decomposition of the reconstructed received signal covariance matrix with the improved weighted subspace method.The algorithm makes full use of cross-correlation information to construct a new received signal covariance matrix,and uses a new correction method for noise subspace information to transform the noise eigenvalues.Then the noise sub-space is weighted,and finally combined with the signal subspace weighting technology.Simulation results show that the improved algorithm can distinguish adjacent targets with an interval of 4 ° under the conditions of low sig-nal-to-noise ratio and small snapshots.Statistical analysis shows that the resolution of the proposed algorithm is significantly better than that of the classical MUSIC algorithm.
关键词
波达方向估计/MUSIC算法/奇异值分解/噪声子空间/高分辨率Key words
direction of arrival estimation/MUSIC algorithm/singular value decomposition/noise sub-space/high resolution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据