@inproceedings{8434d4af67514dfc8120854a795e2a33,
title = "Research on Prediction Method of UAV Heat Seeking Navigation Control Based on GRU Networks",
abstract = "This paper establishes the thermal updraft prediction model based on GRU (Gated Recurrent Unit) network and complete the network training. GRU network is a simplified evolution version of LSTM network. The thermal updraft predictor based on Gru network is designed, and the network structure parameters and network training parameters are given, focusing on the differences with LSTM network. The experiment of this paper is given, which mainly compares the prediction accuracy and running speed of two kinds of predictors under the straight-line flight mode of UAV between LSTM network and GRU network. The experimental results show that in the task of thermal updraft prediction, GRU network can obtain the prediction accuracy similar to LSTM network, but it can reduce a lot of time. Compared with LSTM network, GRU network is more suitable for engineering application.",
keywords = "GRU, LSTM, Thermal updraft prediction, UAV",
author = "Dapeng Zhou and Yang Zhang and Yuangan Li and Ke Li and Bin Zhao and Meixian Wang and Ning Wang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2022 ; Conference date: 05-08-2022 Through 07-08-2022",
year = "2023",
doi = "10.1007/978-981-19-6613-2\_377",
language = "英语",
isbn = "9789811966125",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3874--3881",
editor = "Liang Yan and Haibin Duan and Yimin Deng and Liang Yan",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control",
address = "德国",
}