Multi-Source Information Fusion for Environmental Perception of Intelligent Vehicles Using Sage-Husa Adaptive Extended Kalman Filtering

With the rapid advancement of intelligent driving technology, multi-source information fusion has become a vital topic in the field of environmental perception. To address the fusion deviation resulting from changes in sensor performance due to environmental variations, this paper proposes a multi-source information fusion algorithm based on the improved Sage-Husa adaptive extended Kalman filtering (SHAEKF) algorithm. First, a multi-source information fusion system is constructed based on the vehicle kinematic model and the sensor measurement model. Then, the Sage-Husa adaptive fading extended Kalman filtering (SHAFEKF) algorithm is constructed by introducing a fading factor into the SHAEKF algorithm to enhance the influence of newly incoming data. Finally, the experimental results indicate that the positional average errors of the algorithm in the two scenarios are 0.137 and 0.071. When compared to the SHAEKF algorithm, the positional average errors have been reduced by 2.8% and 13.4%, while the mean squared errors have decreased by 64% and 72%. This demonstrates that the SHAFEKF algorithm offers high accuracy and low fluctuation, enhancing its adaptability in multi-source information fusion systems.