Hypersonic Vehicle Trajectory Classification Using Convolutional Neural Network

被引：6

作者：

Gaiduchenko, Nikolai E. ^{[1
]}

Gritsyk, Pavel A. ^{[1
]}

机构：

[1] Moscow Inst Phys & Technol, Moscow, Russia

来源：

2019 INTERNATIONAL CONFERENCE ON ENGINEERING AND TELECOMMUNICATION (ENT) | 2019年

关键词：

hypersonic vehicles; convolutional neural networks; trajectory classification; flight path classification; time series classification; hypersonic aircraft; radar target recognition; hypersonic vehicle recognition;

D O I：

10.1109/ent47717.2019.9030537

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a Hypersonic Convolutional Neural Network (HCNN) for hypersonic aircraft classification based on a vehicle flight path. The experiments on synthetic data show that HCNN has a high-resolution of identification capability on three types of targets (ballistic missile, hypersonic glide vehicle, hypersonic cruise missile) even in conditions of increased measuring errors, increased time step, and with no preliminary primary data processing. Moreover, one can easily adjust the proposed network for discrimination of an enlarged number of vehicle types.

引用

页数：4

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