Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises
Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises
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DOI：
10.1016/j.sigpro.2009.03.020
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发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
F. Ding;P. X. Liu;Guangjun Liu
中科院分区：
文献类型：
--
作者：
F. Ding;P. X. Liu;Guangjun Liu
For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multi-innovation extended stochastic gradient algorithm by using the auxiliary model method and by expanding the scalar innovation to an innovation vector. Compared with single innovation extended stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm this conclusion.
