Abstract
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.
Highlights
In recent years, with the rapid development of the technology, unmanned aerial vehicle (UAV) have been widely used in many aspects of life, such as target recognition, target tracking, city monitoring, and so on
The high positioning precision of Unmanned aerial vehicle (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
A multi-sensor information fusion method based on GPS, inertial navigation system (INS), and the visible light sensors is proposed for UAV positioning
Summary
With the rapid development of the technology, unmanned aerial vehicle (UAV) have been widely used in many aspects of life, such as target recognition, target tracking, city monitoring, and so on. The measuring frequency of GPS is low and cannot satisfy the real-time demand of UAV positioning system. INS module has strong anti-external interference capability, and the measuring frequency is much higher than GPS The positioning information of GPS can be used to correct the INS errors based on data fusion technology, which can improve the accuracy of INS measurement. Kalman filter algorithm is an algorithm that is based on recursive estimation theory and the mean square error theory. The basic theory of linear discrete Kalman filter is applied to the data estimation and flight control for UAV positioning. We use Kalman filter to estimate the position error based on GPS (or INS) and the visible light sensors, respectively. The trajectory of UAV is corrected according to the position error information, so UAV positioning accuracy is improved in case of only INS being available, and the UAV lifetime is prolonged
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