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A fast algorithm for AR parameter estimation using a novel noise-constrained least-squares method
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DOI：10.1016/j.neunet.2009.11.004.png)
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Abstract
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In this paper, a novel noise-constrained least-squares (NCLS) method for online autoregressive (AR) parameter estimation is developed under blind Gaussian noise environments, and a discrete-time learning algorithm with a fixed step length is proposed. It is shown that the proposed learning algorithm converges globally to an AR optimal estimate. Compared with conventional second-order and high-order statistical algorithms, the proposed learning algorithm can obtain a robust estimate which has a smaller mean-square error than the conventional least-squares estimate. Compared with the learning algorithm based on the generalized least absolute deviation method, instead of minimizing a non-smooth linear L-1 function, the proposed learning algorithm minimizes a quadratic convex function and thus is suitable for online parameter estimation. Simulation results confirm that the proposed learning algorithm can obtain more accurate estimates with a fast convergence speed. (C) 2009 Elsevier Ltd. All Fights reserved.
Keywords:
Autoregressive parameter estimation
Noise-constrained least-squares method
Fast learning algorithm
Fixed step length
Robust estimate
Mean-square error
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