Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

In this paper, a novel attitude estimation for unmanned aerial vehicle (UAV) is proposed based on long and short term memory neural network (LSTM NN). The UAV is a strong coupling and multi-variable nonlinear complex system, in which the attitude estimation is nonlinear and the attitude data of the UAV is a time series sequence. LSTM NN is therefore selected due to its satisfied performance in time-based data prediction. The data samples to train the LSTM NN are collected during the test flight of a quadrotor. To improve the accuracy of the model, different configurations of the LSTM NNs are used for comparison. Experimental results demonstrate that the method for the UAV attitude estimation has higher accuracy and the potential of applying deep learning technique to the online UAV attitude estimation.