El-Sheimy, NaserElkholy, Mohamed2024-01-192024-01-192024-01-17Elkholy, M. (2024). Radar and INS integration for enhancing land vehicle navigation in GNSS-denied environment (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/117995https://doi.org/10.11575/PRISM/42839Nowadays, recent research is interested in autonomous navigation applications. GNSS/INS integration is the most common integration technique to estimate the vehicle navigation state with high accuracy. However, the GNSS/INS fusion technique is not reliable in different environments since the GNSS signal suffers from multipath errors in the downtown area, or the GNSS signal could be blocked in underground parking or indoors. In case of a GNSS signal outage, the navigation system will rely on INS as a standalone solution. INS can estimate the vehicle’s navigation states (position, velocity, and attitude) with high accuracy. However, the INS solution deteriorates with time because of the INS drift. Therefore, other sensors are used to aid IMU in correcting the IMU drift and estimate the vehicle’s navigation states with high accuracy. Vision, Light Detection and Range (LIDAR), odometer can be used to aid INS. However, each sensor has some limitations since these sensors cannot work in different light and weather conditions. Motion constraints, e.g., non-holonomic constraints (NHC), Zero velocity Update (ZUPT), and zero integrating heating rate (ZIHR), can improve the vehicle’s position and limit the IMU drifts. Radio Detection and Ranging (Radar) is known as an all-weather sensor since it can work in different light and weather conditions. Radar is mainly used as Adaptive Cruise Control (ACC) to estimate the range and relative speed of the front vehicle. In terms of continuity, radar, and IMU are the only two sensors that can work in different weather and light conditions to estimate the vehicle’s position. Therefore, this research is focused on the integration between radar and IMU for land vehicle navigation applications. A 360-degree radar was utilized to detect the surrounding objects. Oriented Fast and Rotated Brief (ORB) was adopted to detect and match the features between radar frames. The matched features were exploited to estimate the transition and rotation between radar frames. Then, the radar solution is integrated with INS to improve the navigation solution. Another technique applied in this research to find the corresponding features between radar frames was developed. With the help of the corresponding points, the vehicle’s ego-motion can be estimated. Frequency Modulated Continuous Wave (FMCW) radar was used in this research to estimate the vehicle’s forward speed using the Doppler frequency information. Finally, tightly coupled integration between FMCW radar and IMU was applied to improve the vehicle’s navigation states.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Autonomous NavigationINSRadarRadar/INS IntegrationEKFTightly Coupled IntegrationLoosely Coupled IntegrationEngineering--Automotivedoctoral thesis