期刊
DIGITAL SIGNAL PROCESSING
卷 20, 期 3, 页码 750-762出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2009.09.002
关键词
Recursive identification; Parameter estimation; Stochastic gradient; Multi-innovation identification; Auxiliary model identification idea; Least squares; Convergence properties; Martingale convergence theorem
This paper combines the multi-innovation identification theory and the auxiliary model identification idea and presents an auxiliary model based multi-innovation stochastic gradient algorithm by expanding the scalar innovation to an innovation vector and introducing the innovation length. Convergence analysis in the stochastic framework indicates that the parameter estimates given by the proposed algorithm can fast converge to their true values. Finally, we illustrate and test the proposed algorithm with an example. (C) 2009 Elsevier Inc. All rights reserved.
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