Journal
IEEE ACCESS
Volume 7, Issue -, Pages 41154-41163Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2905576
Keywords
Parameter estimation; maximum likelihood; data filtering; multivariate system
Categories
Funding
- National Natural Science Foundation of China [61873111, 61803183]
- 111 Project [B12018]
- Graduate Education Innovation Program of Jiangsu Province [KYCX17_1458]
- Natural Science Foundation of Jiangsu Province [BK20180591]
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The maximum likelihood principle has wide applications in system identification. This paper studies the maximum likelihood identification problems of the multivariate equation-error systems with colored noise. The system is broken down into several subsystems based on the number of the outputs. The key is to transform the subsystem into a controlled autoregressive moving average model and a noise model. Based on the maximum likelihood principle and the data filtering technique, a filtering-based maximum likelihood recursive generalized extended least squares algorithm is presented for estimating the parameters of these two models. For comparison, a maximum likelihood recursive generalized extended least squares algorithm is presented. Finally, the simulation example results confirm the effectiveness of the two algorithms.
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