辨识非平稳过程的改进型最小二乘格型滤波器
A modified Least Squares Lattice filter to identify non stationary process
摘要：
本文提出用带遗忘因子的最小二乘格型滤波器估计噪声过程模型的时变参数。我们模拟了一个自回归（AR）噪声过程，其中我们让AR的参数随时间变化。研究了最小二乘格型滤波器在跟踪非平稳随机过程时间序列中的一种新的实现方法。此外，我们还引入了一种改进的最小二乘格型滤波器来消除时间序列的非平稳性。我们将此算法应用于识别的实时时间序列数据所产生的录音的声音。
英文摘要：
In this paper the author proposes to use the Least Squares Lattice filter with forgetting factor to estimate time-varying parameters of the model for noise processes. We simulated an Auto-Regressive (AR) noise process in which we let the parameters of the AR vary in time. We investigate a new way of implementation of Least Squares Lattice filter in following the non stationary time series for stochastic process. Moreover we introduce a modified Least Squares Lattice filter to whiten the time-series and to remove the non stationarity. We apply this algorithm to the identification of real times series data produced by recorded voice.
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