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Sensor fusion in vehicle localisation and tracking is a powerful technique that combines multiple data sources for enhanced accuracy. This project applies and compares two TDOA sensor networks and WLS and Kalman Filter based localisation and tracking techniques.
Files created to the Identificazione dei Sistemi Incerti project. Implemented Kalman Filter, EKF, UKF and a smoother. The Matlab files contain also the white-noise charaterzation of the signal and the outliers identification.
A MATLAB and Simulink project. Includes controller design, Simscape simulation, and sensor fusion for state estimation. By: Matteo Liguori; Supervisor and Collaborator: Francesco Ciriello Professor at King's College London
The Matlab scripts for five positioning algorithms regarding UWB localization. The five algorithms are Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Taylor Series-based location estimation, Trilateration, and Multilateration methods.