کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
697491 | 890372 | 2008 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Optimal and suboptimal smoothing algorithms for identification of time-varying systems with randomly drifting parameters Optimal and suboptimal smoothing algorithms for identification of time-varying systems with randomly drifting parameters](/preview/png/697491.png)
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 Cramér–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.
Journal: Automatica - Volume 44, Issue 7, July 2008, Pages 1718–1727