摘要：A new time-varying autoregressive (TVAR) modeling approach is proposed for non-stationary signal processing and analysis. In the new parametric modeling frame work, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The realization of the time-varying AR(TVAR)model here is distinguished from existing time-varying parametric models where the relevant time-dependent coefficients are represented using basis function expansions. In most existing time-varying parametric models, the basis functions used for representing the time-dependent coefficients are global, while the basis functions involved in the new proposed modeling approach are locally defined. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. The associated time-varying coefficients are then estimated by using a Orthogonal least square (OLS) Algorithm. Simulation results show the effectiveness of the proposed method.
关键词：TVAR model; Time-dependent coefficients; Multi-wavelet basis; Orthogonal least square(OLS).
