EUSOF

The ZIM project focuses on developing an innovative method for precise positioning during tram test runs. The aim is to improve the accuracy of vehicle positioning by utilising various signal sources such as GPS, speed signals and inertial sensors. The correct combination of these data sources plays a key role, particularly during test runs where the vehicle’s position must be recorded continuously and reliably.

To ensure position determination even under difficult conditions, such as in tunnels or when GPS signals are interrupted, advanced data fusion algorithms are employed. These include the Kalman filter and the particle filter, both of which are based on statistical models. These filters make it possible to calculate the vehicle’s precise position from inaccurate or temporarily incomplete sensor data. The Kalman filter continuously optimises the position estimate based on new measurement data and model predictions, whilst the particle filter uses a probabilistic method to model even complex situations where uncertainties play a major role.

The application of these methods not only improves the accuracy of road tests but also enables position data to be processed in real time and dynamically adapted to changing circumstances. By combining various sources of information, the aim is to achieve greater positional accuracy without the need to purchase additional measuring instruments.

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