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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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Proceedings ArticleDOI
01 Jan 1992
TL;DR: In this article, a time-domain method was proposed to identify a state space model of a linear system and its corresponding observer/Kalman filter from a given set of general input-output data.
Abstract: This paper presents a time-domain method to identify a state space model of a linear system and its corresponding observer/Kalman filter from a given set of general input-output data. The identified filter has the properties that its residual is minimized in the least squares sense, orthogonal to the time-shifted versions of itself, and to the given input-output data sequence. The connection between the state space model and a particular auto-regressive moving average description of a linear system is made in terms of the Kalman filter and a deadbeat gain matrix. The procedure first identifies the Markov parameters of an observer system, from which a state space model of the system and the filter gain are computed. The developed procedure is shown to improve results obtained by an existing observer/Kalman filter identification method, which is based on an auto-regressive model without the moving average terms. Numerical and experimental results are presented to illustrate the proposed method.

32 citations

Journal ArticleDOI
TL;DR: This paper addresses the parameter and state estimation problem in the presence of the observer gain perturbations for Lipschitz systems that are linear in the unknown parameters and nonlinear in the states with a nonlinear adaptive resilient observer.
Abstract: This paper addresses the parameter and state estimation problem in the presence of the observer gain perturbations for Lipschitz systems that are linear in the unknown parameters and nonlinear in the states. A nonlinear adaptive resilient observer is designed, and its stability conditions based on the Lyapunov technique are derived. The gain for this observer is derived systematically using the linear matrix inequality approach. A numerical example and a physical setup are provided to show the effectiveness of the proposed method.

32 citations

Proceedings ArticleDOI
11 Aug 2009
TL;DR: The experimental results show that proposed Dynamic Kalman Filter shows better results than theTypicalKalman Filter and the Adaptive Kalman filter that is proposed to overcome occlusion problem in the video sequence.
Abstract: In this paper, we propose the ball tracking methodthat is tracking the ball adaptively and robustly in thesoccer video. In the latest works, people have used theTypical Kalman Filter to track the ball. But when theball is disappearing due to the occlusion with players,Typical Kalman Filter has no choice but to make a poolprediction and especially if the player take the ball for along time, the error is produced much more. Toovercome these problems, we propose the DynamicKalman Filter algorithm. Dynamic Kalman Filterrobustly tracks a ball in the dynamic condition by usingplayer information and reduces the error in the situationof occlusion by controlling the velocity of the statevector. The experimental results show that proposedDynamic Kalman Filter shows better results than theTypical Kalman Filter and the Adaptive Kalman Filterthat is proposed to overcome occlusion problem in thevideo sequence.

32 citations

Journal ArticleDOI
TL;DR: In the SREKF algorithm, the EKF's failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis, and an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated.

32 citations

Book ChapterDOI
01 Jan 2017
TL;DR: In this article, the estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion is considered.
Abstract: The estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion is considered in this chapter. Diffuse analogues of the Kalman filter and the extended Kalman filter are obtained. As a practical application, the problems of constructing the filter with a sliding window, observers restoring state in a finite time, recurrent neural networks training and state estimation of nonlinear systems with partly unknown dynamics are considered.

32 citations


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