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Ding, Feng; Liu, Peter X.; Liu, Guangjun

Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises. (English) Zbl 1178.94137

Signal Process. 89, No. 10, 1883-1890 (2009).
Summary: 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.

Cited in 75 Documents

MSC:

94A13 Detection theory in information and communication theory
93E10 Estimation and detection in stochastic control theory
62F99 Parametric inference

Keywords:

parameter estimation; multi-innovation identification theory; LMS; stochastic gradient; output error models; output error moving average (OEMA) models
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\textit{F. Ding} et al., Signal Process. 89, No. 10, 1883--1890 (2009; Zbl 1178.94137)
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References:

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