Identification of System with Non-stationary Signal Using Modified Wilcoxon Approach
Non-stationary random signals exhibit time-dependent characteristics and require proper models and corresponding identification methods. The focus is on identification method. We study system identification of the non-stationary parameters in this task. In this paper, the problem of non-causal identification of non-stationary, linear stochastic systems has been considered. A robust system identification approach adapted to chirp signals is proposed. An asymptotically unbiased estimate for the system’s transfer function is analyzed. We show that compared to a competing non-stationarity based method, a significantly smaller error variance is achieved and generally shorter observation intervals are required. The adaptive method used here, is Wilcoxon approach based. Also the comparison have been done with Sign WLMS and Sign sign WLMS methods as the modified technique. In case of a time-varying system, faster convergence and higher reliability of the system identification are obtained. The results confirm the advantages of proposed approach. The resulting parallel estimation scheme automatically adjusts its smoothing parameters to the unknown, and possibly time-varying, rate of non-stationarity of the identified system.
KeywordsSystem Identification non-stationary signal adaptive signal processing chirp impulse response Wilcoxon norm Sign Wilcoxon Technique
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