A novel Kalman filter for vehicle localization subject to measurement outliers

In this technical note, we investigate a novel Kalman Filter for vehicle localization with explicit mechanisms to address measurement outliers. Specifically, by applying the saturation mechanism to the innovation, the state update phase is corrected under the adaptive adjustment of the saturation bounds, thus restraining the influence of outliers. As for modeling error, an improved Sage–Husa adaptive filtering strategy is introduced to dynamically update the statistical properties of the measurement noise.