中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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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