Autoregressive modeling of the Wigner-Ville distribution based on signal decomposition and modified group delay

Abstract

An autoregressive modeling of the Wigner-Ville distribution (WVD), based on signal decomposition (SD) by a perfect reconstruction filter bank (PRFB) and the modified magnitude group delay function (MMGD), has been proposed. The SD and MMGD, respectively, reduce the existence of crossterms (without any time smoothing) and the Gibb's ripple effect (due to truncation of the WVD kernel, without applying any window), significantly. In view of this, the modeling is not affected by either the crossterms or the Gibb's ripple and the window that would have been used. The proposed method represents actual time-frequency information parsimoniously and compared to the existing WVD modeling methods, its performance is significantly better in terms of both time and frequency resolution (as there is no time and frequency smoothing) and noise immunity/variance and is computationally efficient. � 2003 Elsevier B.V. All rights reserved.