NSHV trajectory prediction algorithm based on aerodynamic acceleration EMD decomposition

doi:10.23919/JSEE.2021.000011

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (1): 103-117.doi: 10.23919/JSEE.2021.000011

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

NSHV trajectory prediction algorithm based on aerodynamic acceleration EMD decomposition

Fan LI^1,^2,*(), Jiajun XIONG¹(), Xuhui LAN¹(), Hongkui BI¹(), Xin CHEN¹

¹ Air Force Early Warning Academy, Wuhan 430019, China
² Unit 95980 of the PLA, Xiangyang 441000, China

Received:2019-09-23 Online:2021-02-25 Published:2021-02-25
Contact: Fan LI E-mail:1746338543@qq.com;13871163420@139.com;Lansoft007@sohu.com;bhk001@126.com
About author:|LI Fan was born in 1992. He received his B.S. and M.S. degrees in radar engineering and signal processing from the Air Force Early Warning Academy. He is now an engineer at Unit 95980 of the PLA and is working toward his Ph.D. degree. His research interests include target detection and tracking, nonlinear filtering, and information fusion. E-mail: 1746338543@qq.com||XIONG Jiajun was born in 1961. He received his Ph.D. degree in software engineering from Huazhong University of Science and Technology, Wuhan, China, in 2004. He is currently a professor with the Air Force Early Warning Academy, Wuhan, China. His research interests include data fusion and early warning intelligence analysis. E-mail: 13871163420@139.com||LAN Xuhui was born in 1976. He received his Ph.D. degree in software engineering in 2012 from the Air Force Early Warning Academy, Wuhan, China, in 2012. He is currently an associate professor with Air Force Early Warning Academy. His research interests include multisensor data fusion and integrate electronic information system. E-mail: Lansoft007@sohu.com||BI Hongkui was born in 1964. She received her B.S and M.S degrees from Huazhong University of Science and Technology in 1986 and 1989, respectively. Now, she is a professor in Air Force Early Warning Academy. Her research interests include radar data processing, and radar equipment technology and application. E-mail: bhk001@126.com||CHEN Xin was born in 1981. He received his Ph.D. degree in cryptology from National University of Defence and Technology, Hunan, China, in 2010. He is currently a teacher with Air Force Early Warning Academy, Wuhan, China. His research interests include big data analysis and cloud computing. E-mail: cxin917@126.com
Supported by:
This work was supported by the National High-Tech R&D Program of China (2015AA7056045; 2015AA8017032P), and the Postgraduate Funding Project (JW2018A039)

Abstract

Abstract:

Aiming at the problem of gliding near space hypersonic vehicle (NSHV) trajectory prediction, a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition (EMD) is proposed. The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule. On this basis, EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items, and aggregate sub-items with similar attributes. Then, a prior basis function is set according to the aerodynamic acceleration stability rule, and the aggregated data are fitted by the basis function to predict its future state. After that, the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory. Finally, experiments verify the effectiveness of the method. In addition, the distribution of prediction errors in space is discussed, and the reasons are analyzed.

Key words: hypersonic vehicle, trajectory prediction, empirical mode decomposition (EMD), aerodynamic acceleration

Fan LI, Jiajun XIONG, Xuhui LAN, Hongkui BI, Xin CHEN. NSHV trajectory prediction algorithm based on aerodynamic acceleration EMD decomposition[J]. Journal of Systems Engineering and Electronics, 2021, 32(1): 103-117.

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Parameter	Value
$m$ /kg	$907.2$
${\rho _0}$ /( ${\rm{kg}}\cdot{{\rm{m}}^{ - 3}} $ )	$1.225$
$V$ /(m/s)	5000
$\theta $ /rad	0
$\lambda $ /rad	0
$\sigma $ /rad	$4/9{\text{π}}$
$\phi $ /rad	0
$h$ /km	50
${S_{re}}$ / ${{\rm{m}}^2} $	0.8839
Observation point geographic coordinate	12°, 15°, 1 000 m

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NSHV trajectory prediction algorithm based on aerodynamic acceleration EMD decomposition

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