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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: A new interacting multiple (IM) filter containing simplified unscented Kalman filter -based subfilters with different heading initializations for a low-performance inertial sensors-based inertial navigation system (INS)/global positioning system (GPS) -integrated navigation system tolerant toward large initial heading error is proposed.
Abstract: This paper proposes a new interacting multiple (IM) filter containing simplified unscented Kalman filter (UKF) -based subfilters with different heading initializations for a low-performance inertial sensors-based inertial navigation system (INS)/global positioning system (GPS) -integrated navigation system tolerant toward large initial heading error. Since each individual subfilter of the IM filter is updated adaptively using the combined information of the estimates from the subfilters, it can converge into a true steady state irrespective of the initial heading of a vehicle containing the navigation system. Thereby the IM filter can provide a stable navigation solution. For the subfilters of the IM filter, a simplified UKF is presented. This has a mixed structure of the extended Kalman filter and UKF and has a lighter computational load than the UKF in the multirate INS/GPS integration. Also, simplified UKF-based subfilters for the INS/GPS-integrated navigation system are designed. Monte Carlo simulations are performed to validate the performance of the proposed IM filter, and an experiment is carried out to confirm the simulation results.

26 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: In this work the problem of orientation tracking based on inertial/magnetic sensors is restated in geometric terms, in particular an intrinsic observer, i.e. an observer whose performance does not depend on a specific choice of coordinates, is derived on the Lie group of rigid body rotations SO(3).
Abstract: In this work the problem of orientation tracking based on inertial/magnetic sensors is restated in geometric terms, in particular an intrinsic observer, i.e. an observer whose performance does not depend on a specific choice of coordinates, is derived on the Lie group of rigid body rotations SO(3). Measurements of the gravitational and geomagnetic fields are used to estimate orientation errors. A coordinate-free control law is defined on the Lie algebra and fed back in terms of angular velocity that steers the observer towards the correct attitude. A proof of stability for the proposed estimator is provided which relies on the natural (bi-invariant) metric of SO(3). The observer results stable for almost the whole configuration space. Presence of unstable equilibria as a limitation for global stability is also discussed. Based on the proposed intrinsic control law, a filter is designed which implements the observer. Simulations are presented that test the numerical implementation of the proposed observer.

26 citations

Journal Article
TL;DR: Nonlinear Kalman filters are algorithms that approximately solve the Bayesian filtering problem by employing the measurement update of the linear Kalman filter (KF).
Abstract: Nonlinear Kalman filters are algorithms that approximately solve the Bayesian filtering problem by employing the measurement update of the linear Kalman filter (KF). Numerous variants have been dev ...

26 citations

Proceedings ArticleDOI
03 Jun 2009
TL;DR: In this article, the authors compared the performance of the Extended Kalman Filter (EKF), Unscented Kalman filter (UKF), and the Divided Differences of 1st and 2nd order (DD1 and DD2) in terms of accuracy and consistency.
Abstract: Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the Extended Kalman filter (EKF), the Unscented Kalman Filter (UKF) and the Divided Differences of 1st and 2nd order (DD1 and DD2). We have compared these filters using the same experimental data. The results obtained are aimed at ranking these approaches by their performances in terms of accuracy and consistency.

26 citations

Journal ArticleDOI
01 Aug 2006
TL;DR: In this article, the authors present a method for designing a non-linear (i.e. extended) Kalman filter that is also parameter adaptive and hence capable of online identification of its model.
Abstract: The paper presents a method for designing a non-linear (i.e. extended) Kalman filter that is also parameter adaptive and hence capable of online identification of its model. The filter model is deliberately simple in structure and low order, yet includes non-linear, load-varying tyre force calculations to ensure accuracy over a range of test conditions. Shape parameters within the (Pacejka) tyre model are adapted rapidly in real time, to maintain excellent state reconstruction accuracy, and provide valuable real-time lateral and vertical tyre force information. The filter is tested in both simulated and test vehicle environments and provides good results. The paper also provides an illustration of the importance of good Kalman filter design practice in terms of selection and tuning of the noise matrices, particularly in terms of the influence of model/sensor error cross-correlations.

26 citations


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