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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 article, a study of three timescales formed from a Kalman filter operating on a model of a clock ensemble is presented, and an optimality property is proved for the reduced Kalman scale.
Abstract: This is a study of three timescales formed from a Kalman filter operating on a model of a clock ensemble. The raw Kalman scale is unstable at short averaging times. The Kalman-plus-weights and reduced Kalman scales are stable at all averaging times. An optimality property is proved for the reduced Kalman scale.

47 citations

Journal ArticleDOI
TL;DR: In this article, the authors present an approach to test and validate a number of power system dynamic state estimation (PSDSE) algorithms, utilizing Real Time Digital Simulator (RTDS), a real-time simulation tool.
Abstract: This paper presents an approach to test and validate a number of power system dynamic state estimation (PSDSE) algorithms, utilizing Real Time Digital Simulator (RTDS), a real-time simulation tool. WSCC 9-bus and IEEE 30-bus systems have been utilized to demonstrate the proposed approach. The test systems are developed on the RSCAD software of the RTDS. The conventional remote terminal unit (RTU) measurements and the phasor measurement unit (PMU) measurements are sent from the RTDS runtime environment to the MATLAB based PSDSE application at regular intervals for the estimation of the states, using software-in-the-loop (SIL) configuration. The PSDSE is solved by utilizing the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the cubature Kalman filter (CKF) approaches, and their relative performances are studied with the help of the simulation results on the two test systems.

47 citations

Journal ArticleDOI
TL;DR: The proposed hybrid observer provides fixed-time convergence of the state estimation error, i.e. there exists a convergence time that is bounded and such a bound is independent of the initial estimation error.

46 citations

Journal ArticleDOI
TL;DR: In this paper, a federated unscented Kalman filter is proposed for attitude estimation in nonlinear spacecraft systems, which provides better performance than the Extended Kalman Filter (EKF).
Abstract: We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

46 citations

01 Aug 1977
TL;DR: Improved accuracy of measured data was obtained when the data were corrected for estimated bias errors and the computing algorithm includes an analysis of residuals which can improve the filter performance and provide estimates of measurement noise characteristics for some aircraft output variables.
Abstract: An extended Kalman filter smoother and a fixed point smoother were used for estimation of the state variables in the six degree of freedom kinematic equations relating measured aircraft responses and for estimation of unknown constant bias and scale factor errors in measured data. The computing algorithm includes an analysis of residuals which can improve the filter performance and provide estimates of measurement noise characteristics for some aircraft output variables. The technique developed was demonstrated using simulated and real flight test data. Improved accuracy of measured data was obtained when the data were corrected for estimated bias errors.

46 citations


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