@inproceedings{557395b7b2b7412d9a69f0cd95fab41a,
title = "An Estimation Method for Scramjet Inlet Mach Number and Mass Flow Rate Based on Deep Learning",
abstract = "Accurate estimation of scramjet inlet parameters (including Mach number and air mass flow rate) is essential for hypersonic flight control. These critical scramjet inlet parameters could be obtained by estimating the air data parameters through the inertial navigation system, but they have large errors. The flush air data sensing system is mainly used for post-flight analysis. This paper proposes an estimation method for scramjet inlet parameters based on deep learning. Accurate estimations of air data are not needed. Instead, the measurements of the transducers on the inlet wall are directly used as the input of the artificial neural network, and then the scramjet inlet parameter (Mach number or air mass flow rate) is output. The results show that the estimation accuracy of the scramjet inlet parameters has been greatly improved. This work provides a new idea for the estimation of the scramjet inlet parameters.",
keywords = "Deep learning, Hypersonic flight, Inertial navigation system, Parameter estimation, Scramjet inlet",
author = "Chen Kong and Hao Liu and Cheng Xu and Juntao Chang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9\_24",
language = "英语",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "225--238",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
address = "德国",
}