An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications

Goh, Shu Ting; Zekavat, S.A.; Abdelkhalik, Ossama

doi:10.1002/9781119434610.ch5

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…To utilize a high-gain observer directly, the system described by (1) needs to be transformed into a canonical form. For this transformation to be feasible, the model must be observable [23], provided such a conversion exists and is reversible. We consider the following satate transformation as consider this system (3).…”

Section: Canonical Form Transformationmentioning

confidence: 99%

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG

Zaggaf,

Mansouri,

El Magri

et al. 2024

IJEECS

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This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.

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Section: Canonical Form Transformationmentioning

confidence: 99%

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG

Zaggaf,

Mansouri,

El Magri

et al. 2024

IJEECS

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“…It is a better performance compared to UKF; it improves the state estimation vector [127]. Limited use, just used at specific applications [127].…”

Section: Ckfmentioning

confidence: 99%

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

et al. 2021

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A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

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“…To reduce the cost of LiDAR, several wind speed estimation techniques have been proposed [26][27][28][29][30][31][32][33][34]. The upcoming wind speed is only estimated in extreme wind conditions in [26].…”

Section: Introductionmentioning

confidence: 99%

“…In [27,28], the wind speed probability distribution is considered rather than wind speed estimation in the time domain. Due to the highly nonlinear behaviour of wind turbines, it is not often feasible to apply conventional linear estimators, such as the ones based on Luengerger observers and Kalman filters [28][29][30][31]. The observer introduced in [31] is only designed for a fixed-speed wind turbine rather than a more sophisticated variablespeed turbine model considered in this work.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation

2023

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Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.

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An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications

Cited by 7 publications

References 24 publications

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG

Sampled-data observer design for sensorless control of wind energy conversion system with PMSG

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation

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