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Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, an output-only observer/Kalman filter identification (O3KID) method is proposed for structural health monitoring based on modal parameters, in particular for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic).
Abstract: Summary This paper presents output-only observer/Kalman filter identification (O3KID), an effective method for the identification of the dynamic model of a structure and its underlying modal parameters using only output time histories measured on the field. The method is suitable for structural health monitoring based on modal parameters, in particular, for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic) and therefore is difficult to measure. O3KID is based on a linear-time-invariant state-space model and is derived from an established and successful approach for input–output system identification, known as observer/Kalman filter identification. The paper rigorously proves the applicability of the approach to the output-only case, presents the resulting new algorithms, and demonstrates them via examples on both numerical and experimental data. Copyright © 2014 John Wiley & Sons, Ltd.

44 citations

Journal ArticleDOI
TL;DR: In this paper, a combined observer is synthesized to estimate plant uncertainties and disturbances, which enables robust state estimation for uncertain dynamical systems and simultaneously, provides full-stateto the perturbation observer under output feedback conditions.
Abstract: A combined observer is synthesized bv unifying the conventional linear state estimator and the perturbation observer to estimate plant uncertainties and disturbances. It enables robust state estimation for uncertain dynamical systems and simultaneously, provides full-stateto the perturbation observer under output feedback conditions. The proposed combined observer is very practical since it is given as a recursive discrete-time form with minimal tuning parameters, and it requires no knowledge of the plant uncertainty. A coupled estimation error dynamics is derived, and the related technical issues such as stability and noise sensitivity are addressed. The combined observer setting is also extended to stochastic systems, and the discrete Kalman filter is reformulated by including the perturbation estimate update process. Numerical examples and experimental results validate the proposed schemes.

44 citations

Book ChapterDOI
TL;DR: This method yields least-squares estimates of the noise covariances, which can be used to compute the Kalman filter gain.
Abstract: The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. In this paper, a method for estimating noise covariances from process data has been investigated. This method yields least-squares estimates of the noise covariances, which can be used to compute the Kalman filter gain.

44 citations

Journal ArticleDOI
17 Jan 2013-Energies
TL;DR: In this paper, the authors compared the performance of the particle filter and the constrained extend Kalman particle filter (cEKPF) for turbofan engine health monitoring under rapid faults and general deterioration.
Abstract: Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.

44 citations

Journal ArticleDOI
TL;DR: In this paper, two kinds of gyroless satellite attitude determination algorithms were reviewed namely, vector measurements and Kalman filter based methods, and robust versions of those Kalman filters, which were incorporated with single, and multiple measurement noise scale factors (SMNSF, MMNSF respectively) are investigated and compared in the presence of measurement faults.

44 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202277
20211
201910
201836
2017269