基于神经网络补偿的室内无人机组合导航系统

针对无人飞行器在环境特征突变情况下数据融合的可靠性大幅下降问题，提出了神经网络预测补偿的组合导航算法。首先利用扩展卡尔曼滤波和粒子滤波对激光、光流等传感器得到的数据进行融合，然后采用径向基函数（RBF）神经网络对粒子滤波前后的误差进行预测。当激光数据可靠时，RBF神经网络进行训练学习模式，当激光数据中断或者不可靠时，利用训练后的模型对系统进行误差补偿。利用无人飞行器在室内环境下进行定点和轨迹实验，结果表明补偿后的位置导航信息能够明显降低激光数据不可靠时带来的定位误差。

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

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.