修正的干扰噪声自相关矩阵重构波束形成算法

为改善采样自相关矩阵求逆(SMI)算法中期望信号存在于接收信号所引起的性能下降,提出一种修正干扰噪声自相关矩阵重构(CMR)算法。该算法首先选取采样自相关矩阵特征分解的最小特征值对应的特征向量构造空间分布系数,再对其在非期望信号波达方向上进行累加实现矩阵的重构。当存在相干信号时,可采取先利用特征向量元素对协方差矩阵进行托普利兹化处理实现解相干,再进行矩阵重构的托普利兹矩阵重构(TCMR)算法。计算机仿真与实验结果证明适用于非相干信号条件下的CMR算法与适用于相干信号条件下的TCMR算法具有更好的输出性能。

A modified interference-plus-noise covariance matrix reconstruction algorithm for beamformer

To improve the performance reduction of sample matrix inversion(SMI) algorithm as the desired signal exists in training data,a modified interference-plus-noise covariance matrix reconstructing(CMR) algorithm is proposed in this paper. The algorithm firstly uses the eigenvector corresponding to the minimum eigenvalue of the sample autocovariance matrix to structure the space distribution coefficient,and then accumulates it on the range except the direction of the desired signal to reconstruct the interference-plus-noise covariance matrix. In the presence of coherent signals,the element in the max eigenvector can be utilized to make the covariance matrix a Toeplitz matrix,and then the CMR algorithm(Toeplitz CMR,TCMR) can be used. Simulation and experiment results demonstrate that the CMR algorithm applied in incoherent signal circumstance and the TCMR algorithm applied in coherent signal circumstance have better output performance.