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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
22 Aug 1999
TL;DR: In this paper, two different Kalman filter designs have been evaluated and compared on the common setup where the mobile robot is equipped with a dual encoder system supported by some additional absolute measurements.
Abstract: Kalman filters have for a long time been widely used on mobile robots as a location estimator. Many different Kalman filter designs have been proposed, using models of various complexity. In this paper, two different design methods are evaluated and compared. Focus is put on the common setup where the mobile robot is equipped with a dual encoder system supported by some additional absolute measurements. A common filter type for this setup is the odometric filter, where readings from the odometry system on the robot are used together with the geometry of the robot movement as a model of the robot. If additional kinematic assumptions are made, for instance regarding the velocity of the robot, an augmented model can be used instead. This kinematic filter has some advantages when used intelligently, and it is shown how this type of filter can be used to suppress noise on encoder readings and velocity estimates. The Kalman filter normally consists of a time update followed by one or more data updates. However, it is shown that when using the kinematic filter, the encoder measurements should be fused prior to the time update for better performance.

67 citations

Journal ArticleDOI
TL;DR: A bilInear fault detection observer is proposed for a bilinear system with unknown inputs and the residual vector in the design of the observer is decoupled from the known inputs and is made sensitive to all the faults.

67 citations

Journal ArticleDOI
TL;DR: A derivative-free nonlinear Kalman filtering approach is introduced aiming at implementing sensorless control of the distributed power generators, which provides estimates of the state vector of the PMSG without the need for derivatives and Jacobian calculation.
Abstract: A control method for distributed interconnected power generation units is developed. The power system comprises permanent-magnet synchronous generators (PMSGs), which are connected to each other through transformers and tie-lines. A derivative-free nonlinear Kalman filtering approach is introduced aiming at implementing sensorless control of the distributed power generators. In the proposed derivative-free Kalman filtering method, the generator's model is first subjected to a linearization transformation that is based on differential flatness theory and next state estimation is performed by applying the standard Kalman filter recursion to the linearized model. Unlike Lie algebra-based estimator design methods, the proposed approach provides estimates of the state vector of the PMSG without the need for derivatives and Jacobian calculation. Moreover, by redesigning the proposed derivative-free nonlinear Kalman filter as a disturbance observer, it is possible to estimate at the same time the nonmeasurable elements of each generator's state vector, the unknown input power (torque), and the disturbance terms induced by interarea oscillations. The efficient real-time estimation of the aggregate disturbance that affects each local generator makes possible to introduce a counterdisturbance control term, thus maintaining the power system on its nominal operating conditions.

67 citations

Journal ArticleDOI
TL;DR: In this paper, data assimilation has been applied in an estuarine system in order to implement operational analysis in the management of a coastal zone using a nonlinear state-space model.
Abstract: Data assimilation (DA) has been applied in an estuarine system in order to implement operational analysis in the management of a coastal zone. The dynamical evolution of the estuarine variables and corresponding observations are modelled with a nonlinear state-space model. Two DA methods are used for controlling the evolution of the model state by integrating information from observations. These are the reduced rank square root (RRSQRT) Kalman filter, which is a suboptimal implementation of the extended Kalman filter, and the ensemble Kalman filter which allows for nonlinear evolution of error statistics while still applying a linear equation in the analysis. First, these methods are applied and examined with a simple 1D ecological model. Then the RRSQRT Kalman filter is applied to the 3D hydrodynamics of the Odra lagoon using the model TRIM3D and water elevation measurements from fixed pile stations. Geostatistical modelling ideas are discussed in the application of these algorithms. (Some figures in this article are in colour only in the electronic version)

67 citations


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