提出了基于目标回波自相关矩阵本征值分解提取多目标特征的新方法。
A method extracting multiple target feature by the eigenvalues decomposition of target echo autocorrelation matrix is presented.
此法大大地提高了有效的信噪比(SNR)和自相关矩阵的估计精度。
The effective signal-to-noise ratio (SNR) and the accuracy of autocorrelation estimation are significantly improved through use of this method.
同时，在信号自相关矩阵条件数不好时，该算法仍然可以进行信号的谱估计。
With bad matrix condition number, the proposed algorithm can also be used for the spectral estimation.
分析了用协方差矩阵和自相关矩阵得出的PCA表达是不同的，但是两者的误差是相同的。
We analyses the different result of PCA by using autocorrelation matrix and covariance matrix, and point out that the express of PCA is different but the error are the same.
主要介绍了一种典型的信噪比估计算法，并对信噪比的自相关矩阵奇异值分解估计法进行了研究。
This paper introduces a typical SNR estimation algorithm by the use of autocorrelation matrix singular value decomposition method.
变换域LMS算法能通过正交变换有效降低输入信号自相关矩阵特征值的分散程度，可提高算法的收敛速度；
The transform domain LMS algorithm can reduce the cross-correlation of input signals effectively through orthogonal transforms, so the convergence rate will be improved;
在研究线性滤波lms算法的收敛条件及推导其稳态相对误差的上下限算式时，都用到自相关矩阵的特征值的表达式。
Several eigenvalue expressions are encountered in studying the convergence condition and the lower and upper limit formula of the LMS algorithm which is used in the adaptive filter design.
在研究线性滤波lms算法的收敛条件及推导其稳态相对误差的上下限算式时，都用到自相关矩阵的特征值的表达式。
Several eigenvalue expressions are encountered in studying the convergence condition and the lower and upper limit formula of the LMS algorithm which is used in the adaptive filter design.
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