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首页> 外文期刊>WSEAS Transactions on Signal Processing >Fast Algorithms with low Complexity for Adaptive Filtering
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Fast Algorithms with low Complexity for Adaptive Filtering

机译：低复杂度的自适应滤波快速算法

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The numerically stable version of fast recursive least squares (NS-FRLS) algorithms represent a very important load of calculation that needs to be reduced. Its computational complexity is of 8L operations per sample, where L is the finite impulse response filter length. We propose an algorithm for adaptive filtering, while maintaining equilibrium between its reduced computational complexity and its adaptive performances. We present a new (M-SMFTF) algorithm for adaptive filtering with fast convergence and low complexity. It is the result of a simplified FTF type algorithm, where the adaptation gain is obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. This algorithm presents a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal. Its computational complexity is of 6L and this is considerably reduced to 2L+4P when we use a reduced P-size (PL) forward predictor.
机译：快速递归最小二乘（NS-FRLS）算法的数字稳定版本代表了非常重要的计算负担，需要减少。它的计算复杂度是每个样本8L次操作，其中L是有限脉冲响应滤波器的长度。我们提出一种自适应滤波算法，同时在降低的计算复杂度和自适应性能之间保持平衡。我们提出了一种新的（M-SMFTF）自适应滤波算法，具有快速收敛和低复杂度的特点。这是简化的FTF类型算法的结果，其中仅从前向预测变量中获得自适应增益，并使用新的递归方法来计算似然变量。这种算法对非常长的滤波器（如在回声消除中使用的那些滤波器）的适应性表现出一定的兴趣，因为它降低了复杂性，其数值稳定性以及在语音信号存在下的收敛性。它的计算复杂度为6L，当我们使用减小的P大小（P L）前向预测器时，它的计算复杂度大大降低至2L + 4P。

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