平方根归一化反馈阶梯算法用于移动平均系统的识别

C. Muravchik, M. Morf
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引用次数: 2

摘要

我们提出了一种平方根归一化版本的反馈阶梯算法,用于识别移动平均模型的参数。所需的方程数量从非规范化情况下的8个减少到5个。方程的复杂性增加了,但这个过程是合理的,因为它似乎导致更方便的硬件实现。此外,这种实现将完全类似于已经为前馈阶梯算法(FFLA)提出的CORDIC处理器实现(对于向后和向前残差行)。一个可能的缺点是所使用的三个变量的大小可能大于1。然而,FBLA的本质特征,即能够直接读出-monic-polynomial模型的估计系数,并没有被修改。
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Square root normalized feedback ladder algorithm for the identification of moving average systems
We have presented a square root normalized version of the feedback ladder algorithm for the identification of the parameters of a moving average model. The number of equations needed is reduced from eight in the unnormalized case to just five. The complexity of the equations increases but the procedure is justified because it seems to lead to a more convenient hardware realization. Moreover, this realization would be completely similar (for the backward and forward residuals lines) to the CORDIC processors implementation already proposed for the feedlorward ladder algorithms (FFLA). A possible disadvantage is that three of the variables used may have magnitudes greater than one. However the essential feature of the FBLA, that of being able to read out directly the estimated coefficients of the -monic-polynomial model is not modified.
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VLSI Architecture for signal processing with alternate low-level primitive structures (ALPS) On redefining the optimal least squares filter under floating point operations A convergence analysis of an adaptive underwater passive tracking system Some properties of a family of generalized time-limited window functions Square root normalized feedback ladder algorithm for the identification of moving average systems
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