Gauss-Markov model formulation for the estimation of time-varying signals and systems

Author

Malladi, Krishna Mohan ; Kumar, Ratnam V Raja ; Rao, K. Veerabhadra

Author_Institution

Dept. of Electron. & Electr. Commun. Eng., Indian Inst. of Technol., Kharagpur, India

Volume

1

fYear

1998

fDate

1998

Firstpage

166

Abstract

A Gauss-Markov model is formulated to estimate the model of a nonstationary signal. The nonstationary signal is represented by a time-varying AR model. The time-varying parameters of the model are modeled as stochastic processes. A unified method for the optimal estimation of both the time-varying parameters and their corresponding stochastic model parameters is presented in this work. This method utilises the proposed Gauss-Markov model for the estimation process through the extended Kalman filter (EKF)

Keywords

Kalman filters; Markov processes; autoregressive processes; filtering theory; nonlinear filters; parameter estimation; signal representation; state estimation; time-varying systems; Gauss-Markov model; extended Kalman filter; nonstationary signal representation; optimal estimation; state estimation; stochastic model parameters; stochastic processes; time-varying AR model; time-varying parameters; time-varying signal estimation; time-varying system estimation; unified method; Gaussian noise; Gaussian processes; Integrated circuit modeling; Kalman filters; Seismology; Signal processing; Speech; Stochastic processes; Stochastic resonance; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '98. 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control

Conference_Location

New Delhi

Print_ISBN

0-7803-4886-9

Type

conf

DOI

10.1109/TENCON.1998.797104

Filename

797104