基于神经网络补偿的室内无人机组合导航系统

doi:10.7527/S1000-6893.2019.23790

Abstract

Abstract: Aiming at the problem that the reliability of data fusion will be drastically reduced when the environmental characteristics of the unmanned aerial vehicle are mutated, this paper proposes an algorithm to address the problem based on the prediction and compensation of neural network. First, the extended Kalman filter and particle filter are used for data fusion of laser and optical flow sensor, and then the Radial Basis Function (RBF) neural network is used to estimate the error before and after applying the particle filter. When the laser data is reliable, the RBF neural network enters the learning mode. When the laser data are interrupted or unreliable, the system is compensated by using the trained model. The results of the hover and trajectory experiments of unmanned aerial vehicles in the indoor environment show that when the laser data are unreliable, the compensated position for navigating is still reliable.

Key words: combined navigation system, RBF neural network, extended Kalman filter, particle filter, predictive compensation

CLC Number:

GUAN Xiangzhong, CAI Chenxiao, ZHAI Wenhua, WANG Lei, SHAO Peng. Indoor integrated navigation system for unmanned aerial vehicles based on neural network predictive compensation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(S1): 723790-723790.

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Indoor integrated navigation system for unmanned aerial vehicles based on neural network predictive compensation

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