Adaptation and tracking in system identification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Identification of Time-Varying Processes
Identification of Time-Varying Processes
Brief paper: On the lower smoothing bound in identification of time-varying systems
Automatica (Journal of IFAC)
On "cheap smoothing" opportunities in identification of time-varying systems
Automatica (Journal of IFAC)
A survey of data smoothing for linear and nonlinear dynamic systems
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
Noncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothing is based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman filter-based parameter trackers can be obtained by means of backward-time filtering of the estimates yielded by both algorithms. When system parameters drift according to the random walk model and the adaptation gain is sufficiently small, the properly tuned two-stage Kalman filtering/smoothing algorithm, derived in the paper, achieves the Cramer-Rao type lower smoothing bound, i.e. it is the optimal noncausal estimation scheme. Under the same circumstances performance of the modified exponentially weighted least-squares algorithm is often only slightly inferior to that of the Kalman filter-based smoother.