Title :
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
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;
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
DOI :
10.1109/TENCON.1998.797104
