|Abstract:||To support the autonomous mission capability of unmanned aircraft systems (UAS), developing a navigation system that ensures precise positioning performance during the final approach phase is necessary. Typically, UASs used in general are guided by Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU), fused by loosely coupled form with a Kalman filter, which provides reliable positioning solution to approximately a meter level. However, the meter level of positioning accuracy exceeds the height of most small UASs. Furthermore, accuracy is degraded in GNSS harsh environment owing to signal blockage, multipath, and poor satellite geometry. To overcome such limitations of the loosely coupled GNSS/IMU Kalman filter, this study presents a combination of the tightly coupled GNSS/IMU and ultra-wide band (UWB) measurements obtained from the network setup that is nearby the landing site to ensure safe landing of small UASs. For higher positioning performance of the tightly coupled GNSS/IMU/UWB Kalman filter, we used an experimental air-to-ground error model of UWB ranging characteristics and a Kalman filter with retrodiction to process time-delayed measurements. Then, an outlier detection and rejection based on innovation test is also applied. The positioning accuracy of the proposed method increases as the UAS approaches a touch-down point, which is a desirable positioning characteristic for UAS landing operations. The results based on multiple flight tests showed a 20 cm 3D root mean squared error (RMSE) compared to that of the reference trajectory during the final approach.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||1326 - 1337|
|Cite this article:||
Lee, Cheolmin, Kim, Euiho, "Tightly Coupled GNSS/IMU/UWB Kalman Filter Supporting UAS Autoland in GNSS Harsh Environment," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1326-1337.
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