Recently tremendous efforts aim at the development of satellite-based navigation techniques in combination with further sensors to ensure precise vehicle trajectory determination. The satellite-based navigation with basic GNSS stand-alone processing provides users for a vehicle positioning at several meters accuracy, almost all the time, anywhere on or near the earth's surface. In most geodetic applications, a very precise and continuous knowledge of the navigation solution (e.g. position, velocity, attitude, etc.) of the moving body is required, which cannot be guaranteed by GNSS stand-alone. For example, tracking the train's position is usually realized via an odometer from the on-board system, which measures wheel rotations. The odometer measurements are repeatedly corrected and initialized at fixed locations known as balises. A balise is an electronic beacon or transponder placed between the rails of a railway as part of an automatic train protection (ATP) system. These sensors constitute an integral part of the European Train Control System, where they serve as "beacons" giving the exact location of a train. This method is very popular to provide an accurate along track position of a moving train. However, balises are expensive sensors, which need to be placed over about 250 000 km of train tracks in Europe.
Therefore, recently many activities are around to monitor trains by EGNOS or an ensemble of on-board sensor devices like GNSS/IMU/odometer. Especially the latter might be a costeffective but also accurate approach, especially suited for rail secondary lines. A passenger vehicle or a rail-mounted vehicle can simply be equipped with a GNSS unit, an IMU device and other navigation sensors where differential GNSS can provide the accurate position and velocity, while IMU delivers position increments, velocity and direct spatial orientation of the vehicle. When GNSS signal tracking fails for a short period in time this outage can be almost compensated by processing the observation of the strapdown mounted IMU platform in combination with odometer data.
Over the past years, a loosely coupled Kalman filter algorithm based on the fusion of GNSS, IMU and odometer data was developed by the doctoral candidate. This algorithm enables a reliable train positioning. Also an algorithm based on !MU/odometer integration is implemented, and can be applied in case of GNSS signal outages ( e.g. tunnels and urban areas).
In parallel, also a software package employing a tightly coupled Kalman filter algorithm has been developed. The software allows for the fusion of GNSS and IMU data, which enables a reliable vehicle positioning performance. This approach has been utilized to estimate the trajectories of slow and fast-moving vehicles like cars or trains.