Research on the Algorithm of Information Fusion for Height of UAV

Unmanned aerial vehicle (UAV) measurement accuracy is limited, a single sensor is difficult to obtain accurate information on various states, in order to achieve autonomous unmanned aerial vehicle anti-collision function and perform related tasks. The development of fast unmanned aerial vehicle sensor fusion approach has been one of the key unmanned aerial vehicle areas of anti-collision. In this paper, taking unmanned aerial vehicle flying height parameter as an example, it proposes an unmanned aerial vehicle sensor fusion method based on neural network and consistency fusion. First it uses consistency fusion method for unmanned aerial vehicle height effectively sensor information fusion to obtain fusion results, Second, it uses BP neural network to complete the pitch angle of the information fusion, achieve unmanned aerial vehicle sensor fusion performance. By a certain type of unmanned aerial vehicle real data verification, it shows that the method can improve the accuracy of gaining information and has certain engineering application value.