Nonstationary signal reconstruction from TVAR coefficients

Abstract

Nonstationary signal (NSS) reconstruction from Time Varying (TV) coefficients of Time Varying Autoregressive (TVAR) process is presented in this paper. The proposed method consists of three steps, where in the first step, initial values for TVAR coefficients are estimated from synaptic weights of a three layer Artificial Neural Network (ANN) which is trained using Backpropagation (BP) learning algorithm. The estimated TVAR coefficients are then optimized using a Genetic Algorithm optimization algorithm for more accurate values in the second step. And finally once the TVAR coefficients are estimated using ANN and GA, it is then used to recover the original signal. Performance of proposed method has been evaluated by comparing reconstruction of various computer generated NSS from proposed methods with other methods. Five performance metrics was used for comparison where proposed method is shown to overcome the performance of other methods.