论文部分内容阅读
本文给出了一种加有二次约束条件的自适应横向滤波器。这种自适应横向滤波器利用了无约束自适应横向滤波器Widrow-Hoff算法的基本思想,具有自动搜索输入序列自相关矩阵最小特征值所对应的特征矢量的能力,并且实现简单,从而它可用作最佳运动目标检测及Pisarenko谱分解的自适应实现。分析和计算机模拟实验证实了其自适应能力。这种自适应滤波器的学习时间常数不同于无约束情况,而是反比于特征值的差值。
This paper presents an adaptive transversal filter with quadratic constraints. This adaptive transversal filter makes use of the basic idea of the Widrow-Hoff algorithm for unconstrained adaptive transversal filters and has the ability to automatically search for eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of the input sequence, and is simple to implement For the best detection of moving objects and Pisarenko spectral decomposition adaptive implementation. Analysis and computer simulation confirmed its adaptive ability. The learning time constant of this adaptive filter is different from the unconstrained case, but inversely proportional to the difference of the eigenvalues.