Unmanned aerial vehicle positioning based on multi-sensor information fusion

• Main Authors: Wenjun Li, Zhaoyu Fu
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-10-01
• Series: Geo-spatial Information Science
• Subjects: Kalman filter algorithm; simulated annealing algorithm; target tracking; integrated positioning system
• Online Access: http://dx.doi.org/10.1080/10095020.2018.1465209
• ISSN: 1009-5020; 1993-5153

Unmanned aerial vehicle (UAV) positioning is one of the key techniques in the field of UAV navigation. Although the high positioning precision of UAV can be achieved through global positioning system (GPS), the frequency of updating signal in GPS is low and the energy consumption of GPS module is huge, which does not satisfy the real-time demand of UAV positioning. In this paper, a multi-sensor information fusion method based on GPS, inertial navigation system (INS), and the visible light sensors is proposed for UAV positioning. The Kalman filter combining with simulated annealing algorithm is used to estimate the position error between GPS or INS and the visible light sensors, and then the motion trajectory is corrected according to this position error information. Therefore, the positioning accuracy of UAV can be improved in case of only INS being available. Experimental results demonstrate that the proposed method can remarkably improve the positioning accuracy and greatly reduce the energy consumption.