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Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

机译：基于LSTM神经网络的无人机姿态估计。

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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.
机译：本文基于长期内存神经网络（LSTM NN）提出了一种基于长期和短期内存的新型航空车辆（UAV）的新型姿态估计。 UAV是一个强耦合和多变量非线性复杂系统，其中姿态估计是非线性的，并且UAV的姿态数据是时间序列序列。因此，由于其基于时间的数据预测性的满意性能，因此选择了LSTM NN。在四足电池的测试飞行期间收集训练LSTM NN的数据样本。为了提高模型的准确性，LSTM NNS的不同配置用于比较。实验结果表明，UAV态度估计的方法具有更高的准确性和应用深度学习技术对在线无人机态度估算的可能性。

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