Correct knowledge of noise statistics is essential for an estimator or controller to have reliable performance. In practice, however, the noise statistics are unknown or not known perfectly and thus need to be identified. Previous work on noise identification is limited to stationary noise and noise with slowly varying statistics only. An approach is presented here that is valid for nonstationary noise with rapidly or slowly varying statistics as well as stationary noise. This approach is based on the estimation with multiple hybrid system models. As one of the most cost-effective estimation schemes for hybrid system, the interacting multiple model (IMM) algorithm is used in this approach. The IMM algorithm has two desirable properties: it is recursive and has fixed computational requirements per cycle. The proposed approach is evaluated via a number of representative examples by both Monte Carlo simulations and a nonsimulation technique of performance prediction developed by the authors recently. The application of the proposed approach to failure detection is also illustrated.<>
A recursive multiple model approach to noise identification
IEEE Transactions on Aerospace and Electronic Systems ; 30 , 3 ; 671-684
1994-07-01
1167850 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A Recursive Multiple Model Approach to Noise Identification
Online Contents | 1994
|Frequency-Domain Recursive Robust Identification
AIAA | 2000
|Recursive Mode Star Identification Algorithms
Online Contents | 2005
|Recursive mode star identification algorithms
IEEE | 2005
|Recursive robust identification in frequency-domain
AIAA | 1997
|