LTV System Identification via a Kernel-based Regularization Method under Savitzky-Golay Filtering

An important task in the identification community for time-varying systems is to track the possible changes in system dynamics as well as possible. For the currently applied identification techniques, this task is usually implemented at the cost of increasing the computational quantity as the time increases, or of adding a forgetting factor that requires to be determined a priori. In this paper we develop an estimation approach for linear time-varying systems with additive disturbances, which achieves a major computational advantage without determining the additional factor. In particular, we integrate the idea behind Savitaky-Golay filtering into the kernel-based regularization method under the framework of regularized least squares. It is found that the developed approach remains the same computational quantity during the whole time interval in the estimating procedure. A numerical example is simulated to validate the effectiveness of the developed approach.