Bias compensation based recursive least-squares identification algorithm for MISO system with input and output noises

A bias compensation based least-squares algorithm is proposed for the parameter estimation of multi-input single-output system in the presence of input and output white noises. It is shown that the bias term is induced by the variances of input and output noises. Therefore, an efficient method which...

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Vydáno v:ASCC : 2017 11th Asian Control Conference : 17-20 December 2017 s. 812 - 816
Hlavní autoři: Wu, Ai-Guo, Qi, Wen-Nian, Dong, Rui-Qi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2017
Témata:
Bias compensation
Data models
Estimation
Identification
Indexes
least-squares
MIMO communication
MISO communication
MISO system
Parameter estimation
White noise
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Shrnutí:A bias compensation based least-squares algorithm is proposed for the parameter estimation of multi-input single-output system in the presence of input and output white noises. It is shown that the bias term is induced by the variances of input and output noises. Therefore, an efficient method which uses the observed input and output data directly is developed in this paper to estimate the unknown variances of white noises. The proposed bias compensation based least-squares algorithm can be established from the combination of the recursive least-squares estimation algorithm and white noise variances estimation algorithm. The effectiveness of the proposed algorithm is both analyzed theoretically and verified by a simulation example.
DOI:10.1109/ASCC.2017.8287275