Decoupled Kalman Filter Based Identification of Time-Varying FIR Systems

Abstract

When system parameters vary at a fast rate, identification schemes based on model-free local estimation approaches do not yield satisfactory results. In cases like this, more sophisticated parameter tracking procedures must be used, based on explicit models of parameter variation (often referred to as hypermodels), either deterministic or stochastic. Kalman filter trackers, which belong to the second category, are seldom used in practice due to difficulties in adjusting their internal parameters such as the smoothness coefficient and the order of the hypermodel. The paper presents a new solution to this problem, based on the concept of preestimation of system parameters. The resulting identification algorithms, which can be characterized as decoupled Kalman trackers, are computationally attractive, easy to tune and can be optimized in an adaptive fashion using the parallel estimation approach. The decoupled KF algorithms can be regarded as an attractive alternative to the state-of-the-art algorithms which are much more computationally demanding.

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

“…With the combination of KF and wavelet decomposition, Chen et al [22] proposed an approach for identifying time-variant parameters. Based on the concept of pre-estimation of system parameters, Ciolek et al [23] proposed a decoupled KF approach for the estimation of time-varying systems. To enhance the convergence, Yu et al [24] proposed a modifed EKF for identifying time-variant Hammerstein-Wiener nonlinear systems.…”