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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.


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Journal ArticleDOI
TL;DR: In this paper, the authors investigated the application of the Kalman filter's nonlinear variants, namely the extended Kalman Filter (EKF), unscented KF and second order central difference filter (CDF2), in a low cost strapdown inertial navigation system (SINS) integrated with the global position system (GPS).
Abstract: The Kalman filter is a familiar minimum mean square estimator for linear systems. In practice, the filter is frequently employed for nonlinear problems. This paper investigates into the application of the Kalman filter’s nonlinear variants, namely the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the second order central difference filter (CDF2). A low cost strapdown inertial navigation system (SINS) integrated with the global position system (GPS) is the performance evaluation platform for the three nonlinear data synthesis techniques. Here, the discrete-time nonlinear error equations for the SINS are implemented. Test results of a field experiment are presented and performance comparison is made for the aforesaid nonlinear estimation techniques.

29 citations

Journal ArticleDOI
TL;DR: It will be shown that the fuzzy version of the Kalman filter gives some advantages when is compared with the Extended Kalman Filter (EKF), which is the most typical extension of the KF to the nonlinear field.
Abstract: In this work, the Kalman Filter (KF) and Takagi–Sugeno fuzzy modeling technique are combined to extend the classical Kalman linear state estimation to the nonlinear field. The framework for such extension is given, and in this sense the discrete-time fuzzy Kalman filter (DFKF) is obtained. It will be shown that the fuzzy version gives some advantages when is compared with the Extended Kalman Filter (EKF), which is the most typical extension of the KF to the nonlinear field. The proposed approach provides a significantly smaller processing time than the processing time of the EKF while the mean square error is also reduced. Finally, some examples, such as the Lorenz chaotic attractor and under actuated mechatronic system (pendubot), are used to compare the DFKF and EKF.

29 citations

Journal ArticleDOI
TL;DR: Three popular stochastic filtering techniques are used to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable-stator flux.
Abstract: This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable—stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design.

29 citations

Journal ArticleDOI
J.C. Chung1, Z. Bien1, Y.S. Kim
TL;DR: In this article, the wave-excitation input information is extracted from the estimated ship-motion data, and a prediction is made by extrapolating the governing equation of the ship motion.
Abstract: In order to predict the motion of a ship effectively, a new algorithm is developed in which the wave-excitation input information is extracted from the estimated ship-motion data, and a prediction is made by extrapolating the governing equation of the ship motion. Simulations performed with the algorithm and the conventional ship-motion prediction algorithm based on the standard Kalman filter are compared. >

29 citations


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