Enhanced Land Vehicles Navigation by Fusing Automotive Radar and Speedometer Data

Navigating land vehicles and self-driving cars is vital for a safe and accurate journey. Moreover, it's essential for taking the shortest route to save fuel and protect the environment from excessive pollution mainly, Global Navigation Satellite systems (GNSS) are the fundamental choice for such application. however, In urban canyons, the GNSS suffer from signal blockage due to the highrisebuildings in such environments. therefore, Inertial Navigation Systems(INS) such as reduced inertial sensor sytstem (RISS) are utilized to backup the GNSS during their outages. on the other hand, the RISS solution drifts over time. A Radar-based RISS is utilized to overcome the regular RISS drawbacks by utilizing the adaptive cruise control FMCW radar for speed measuring. in this paper, a fuzzy based fusion algorithm is introduced to improve the forward speed measurements from the speedometer and radar. Also, an integration algorithm using extended kalman filter (EKF) is developed to produce a more precise solution. The proposed system has been tested on a real road trajectory in a urban area and involved various GNSS outages. The results show the capabilities of system in keeping the navigation solution drift to a minimum especially when the GNSS is in outage situation compared with the traditional RISS/GNSS and the Radar-based RISS/GNSS system.