Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural NetWork
文献类型：会议论文
| 作者 | Yaohua Liu; Yimin Zhou; Xiang Li |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | Windsor, BRAZIL |
| 英文摘要 | 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. |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13820]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Yaohua Liu,Yimin Zhou,Xiang Li. Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural NetWork[C]. 见:. Windsor, BRAZIL. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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