一种基于广义特征向量的改进ESPRIT测向算法

An Improved ESPRIT Algorithm Based on Generalized Eigenvectors

基于信号子空间的ESPRIT算法不需要进行谱峰搜索,但是估计方差大于MUSIC算法。该文提出了一种基于广义特征向量的ESPRIT算法,利用信号子空间旋转不变关系矩阵的广义特征向量估计信号波达方向,得到了比基于广义特征值算法更准确的方向估计结果。该算法充分利用了信号子空间的旋转不变特性,通过利用信号子空间与阵列流形的关系进行波达方向估计,实验证明该算法能够在保持小计算量的优势下达到与MUSIC算法相近的性能。

Estimation of signal parameters via rotational invariance techniques （ESPRIT） algorithm does not need spectral peak searching, but the estimated variance is greater than the multiple signal classification （MUSIC） algorithm. This paper proposes a generalized eigenvector-based ESPRIT algorithm, which makes full use of generalized eigenvectors of rotation invariant relationship matrix and obtains a better performance when compared with traditional ESPRIT algorithm. The experimental results show that the proposed algorithm can achieve the similar performance of the MUSIC algorithm while keeping a low computational complexity.