| Summary: | This master’s thesis examines whether it is possible to improve an existing road grade estimator by measuring an additional signal from a GPS receiver. The existing estimator uses the differentiated altitude signal from the GPS to estimate the current road grade. The additional signal is the vertical component of the 3D-velocity that is reported by the GPS. By using spectral analysis together with system identification the additional signal, which is based on velocity information, is compared to the previously used signal which is based on position information (altitude). By sampling data from an experiment at Swedish highway E4 it was possible to conclude whether the two signals should be used together (sensor fusion) or if it was better to use just one of them. The signals’ measurement errors were not white noise but they were modeled as autoregressive (AR) processes. An augmented Kalman filter, with the two AR models included in the system model, was used to evaluate the performance of the sensor fusion algorithm. The result of the thesis is that the previously used signal, the one based on differentiated altitude, should be used alone in this specific road grade estimator. The major reason for this is that the additional signal proved to have a bias that would have been integrated by the road grade estimator and driven the estimation of the altitude away from the true altitude. A solution for this problem would be to neglect this bias and construct a road grade estimator that does not include the altitude as a state.
|
|---|
