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- Title
Time-Varying Autoregressive Model Using Multi-Wavelet Basis Functions.
- Authors
Reddy, G. Ravi Shankar; Rao, Rameshwar
- Abstract
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 timevarying coefficients are then estimated by using a Orthogonal least square (OLS) Algorithm. Simulation results show the effectiveness of the proposed method.
- Subjects
MATHEMATICAL models of time-varying systems; AUTOREGRESSIVE models; MATHEMATICAL models of signal processing
- Publication
Computer Science & Telecommunications, 2015, Vol 46, Issue 2, p47
- ISSN
1512-1232
- Publication type
Article