摘要
基于子空间正交特性的MUSIC算法具有优良的超分辨性能,但由于其需要对空间协方差矩阵进行特征分解,因而计算量比较大。为了降低计算复杂度,提出一种快速子空间算法。该方法利用信号特征值大于噪声特征值的特性,通过对空间协方差矩阵的高阶次幂或者空间协方差矩阵逆的高阶次幂来逼近信号子空间或者噪声子空间,从而避免了特征分解。获得噪声子空间后再采用MUSIC算法实现波达方向估计。仿真结果表明,该方法减少了计算量同时能够达到MUSIC算法的估计性能。
The MUSIC algorithm with sub-space orthogonal characteristics has an excellent super-resolution performance,but it needs to eigendecompose the spatial covariance matrix,which leads to a great computational cost.To reduce the computational complexity,a fast sub-space algorithm is proposed.Making use of the characteristic of signal eigenvalue being larger than noise eigenvalue,this method approximates the signal sub-space or noise sub-space through the high order power of the spatial covariance matrix or the inverse one to avoid the eigendecompsition.After obtaining the noise sub-space,it is capable to get the DOA by a MUSIC algorithm.The simulation result shows that the method achieves the performance of the MUSIC algorithm while reducing the computational cost.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第4期691-693,共3页
Systems Engineering and Electronics
基金
国防基础科研基金(A242006110406)资助课题
