Kalman filter approach for identification of linear fast Time-Varying processes

This paper deals with the use of Kalman filter approach for identification of linear fast time-varying processes. Most of the physical processes exhibit some degree of time-varying behavior. Physical processes exhibit time-varying behavior for a number of reasons. Some of the processes are inherently time-varying and can not effectively be modeled using time invariant models. Recursive identification methods are useful for the parameters estimation while the system is in operation. The model should then be based on observations up to the current time. In online identification the current estimates of the system are constantly being calculated. In this paper we employed first order time-varying (TV) auto-regressive with exogenous (ARX) model and its fast varying parameters are estimated using recursive Kalman filter method. The input is pseudo-random binary sequence (PRBS) which is frequency rich signal. Parameters are estimated for different noise conditions. The performance of the method is evaluated by calculating error performance measures. The results depict the suitability of the method.