Maximum likelihood least squares identification method for active noise control systems with autoregressive moving average noise

Maximum likelihood methods are significant for parameter estimation and system modeling. This paper derives a maximum likelihood principle based least squares identification algorithm for online secondary path modeling in feed-forward active noise control systems with autoregressive moving average n...

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Bibliographic Details
Published in:Automatica (Oxford) 2016-07, Vol.69, p.1-11
Main Author: Aslam, Muhammad Saeed
Format: Article
Language:English
Subjects:
Active noise control
Algorithms
Autoregressive moving average
Cost function
FxLMS
Least squares
Least squares method
Mathematical models
Maximum likelihood
Modelling
Noise
Online secondary path modeling
Parameter estimation
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Summary:Maximum likelihood methods are significant for parameter estimation and system modeling. This paper derives a maximum likelihood principle based least squares identification algorithm for online secondary path modeling in feed-forward active noise control systems with autoregressive moving average noise. This derivation proves that minimizing the cost function of least squares is equivalent to the maximum of likelihood function. Proposed method requires tuning of only one parameter in comparison with other recognized methods. Simulation tests show that proposed algorithm has better estimation accuracy and noise reduction capability than the current state-of-the-art methods in the presence and absence of disturbance at the error microphone.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2016.02.011