Flying Objects Classification Based on Micro-Doppler Signature Data From UAV Borne Radar

被引：1

作者：

Mandal, Priti ^{[1
]}

Roy, Lakshi Prosad ^{[1
]}

Das, Santos Kumar ^{[1
]}

机构：

[1] Natl Inst Technol Rourkela, Rourkela 769008, Odisha, India

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2024年 / 21卷

关键词：

Drones; Radar; Classification algorithms; Arrays; Radar antennas; Doppler radar; Autonomous aerial vehicles; Classification; drone; flying objects; micro-Doppler signature (MDS); radar antenna array;

D O I：

10.1109/LGRS.2024.3354973

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Unmanned aerial vehicles (UAVs) have been widely used in many facets of contemporary society over the past ten years due to their accessibility and affordability. The rise in drone usage brings up privacy and security issues. It is essential to be vigilant for unauthorized UAVs in restricted areas. In this work, a hybrid Convolutional Neural Network-Shuffled Frog Leap (CNN-SFL) algorithm is proposed for classifying various flying objects, such as drones, helicopters, and artificial birds based on micro-Doppler signature (MDS) collected from HB100 radar mounted on UAV. Various array positioning and configuration, such as uniform linear array (ULA), uniform rectangular array (URA), and uniform circular array (UCA), are taken into account when analyzing the accuracy for avoiding performance loss due to a significant angle of arrival (AoA) of the received signal. Further, the activities of drones are also classified, and accuracy is assessed in comparison to existing algorithms. The results demonstrate that the proposed technique outperforms in all cases. In the endfire direction, URA performs better as compared to the other configurations and in other directions, ULA performs better.

引用

页数：5

共 13 条

[1] A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques [J].

Chamola, Vinay ;

Kotesh, Pavan ;

Agarwal, Aayush ;

Naren ;

Gupta, Navneet ;

Guizani, Mohsen .

AD HOC NETWORKS, 2021, 111 (111)

[2] Detection, Tracking, and Interdiction for Amateur Drones [J].

Guevenc, Ismail ;

Koohifar, Farshad ;

Singh, Simran ;

Sichitiu, Mihail L. ;

Matolak, David .

IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (04) :75-81

[3] Drone Elevation Angle Classification Based on Convolutional Neural Network With Micro-Doppler of Multipolarization [J].

Kang, Hyunseong ;

Kim, Byung Kwan ;

Park, Jun-Sung ;

Suh, Jun-Seuk ;

Park, Seong-Ook .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19

[4] Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum [J].

Kang, Ki-Bong ;

Choi, Jae-Ho ;

Cho, Byung-Lae ;

Lee, Jung-Soo ;

Kim, Kyung-Tae .

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 57 (05) :3252-3267

[5]

Kashtiban A. Mashhadi, 2009, 2009 14th International CSI Computer Conference (CSICC 2009) (Postponed from July 2009), P576, DOI 10.1109/CSICC.2009.5349641

[6]

Kumari TS, 2020, IEEE REGION 10 SYMP, P851

[7] DIAT-μSAT: Small Aerial Targets' Micro-Doppler Signatures and Their Classification Using CNN [J].

Kumawat, Harish C. ;

Chakraborty, Mainak ;

Raj, A. Arockia Bazil ;

Dhavale, Sunita Vikrant .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19

[8]

Medaiyese O. O., 2021, 2021 2 INT C SMART C, P58

[9] Deep Neural Network Initialization Methods for Micro-Doppler Classification With Low Training Sample Support [J].

Seyfioglu, Mehmet Saygin ;

Gurbuz, Sevgi Zubeyde .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (12) :2462-2466

[10] Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning [J].

Shin, Hoo-Chang ;

Roth, Holger R. ;

Gao, Mingchen ;

Lu, Le ;

Xu, Ziyue ;

Nogues, Isabella ;

Yao, Jianhua ;

Mollura, Daniel ;

Summers, Ronald M. .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) :1285-1298

← 1 2 →