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为了减少盖氏圆准则信源数估计算法的运算量并且提高信源数估计的精度,根据噪声空间与阵列导向矩阵的正交性原理,设计了基于特征空间的信源数估计算法(Estimator Based on Eigenvectors,EBE).EBE构造时空相关矩阵,利用色噪声在时间上相关性比较弱的特点,实现对空间色噪声的抑制.在空间白噪声环境下和空间色噪声环境下测试了EBE信源数估计的性能并且与传统的盖氏圆准则和其它色噪声类信源数估计的一些算法比较,证明了EBE在空间白噪声和空间色噪声环境下的有效性.EBE不仅节省了盖氏圆准则信源数估计中一次特征分解的运算量,并且同时提高了信源数估计的性能.
In order to reduce the computational complexity of GNS algorithm and improve the accuracy of signal source estimation, a novel estimation algorithm based on eigenspace is proposed according to the orthogonality principle of noise space and array steering matrix on Eigenvectors, EBE) .EBE constructs the spatiotemporal correlation matrix, and utilizes the characteristic that the color noises are weakly correlated in time to achieve the suppression of the spatial color noises.EBE source is tested under the environment of space white noise and space color noises And compared with some algorithms of traditional Gai’s circle criterion and other colored noise class sources estimation, this paper proves the validity of EBE in space white noise and spatial color noise environment.EBE not only saves Gaiyuan circle Criterion Number of source estimates The number of computations for a feature decomposition, and at the same time improves the performance of the estimate of the number of sources.