Title

A Study on Intelligent Anti-Drone System using AI-based Long-Range Detection and Identification

Alternative Title

AI 기반 장거리 탐지 및 식별 기술을 이용한 지능형 안티드론 시스템 연구

DGIST Authors

Woo-Cheol Jin ; Ji-Woong Choi

Advisor

최지웅

Issued Date

2026

Awarded Date

2026-02-01

Type

Thesis

Description

Anti-UAV System, Radar, Camera, RF Scanner, Jammer

Table Of Contents

1. Introduction 1
1.1 Background and Motivation 1
1.2 Components of an Anti-Drone System 2
1.3 Characteristics of Anti-Drone System Components 3
1.4 Contributions of this Dissertation 5
2. Radar: AI-Based Long-Range Detection 8
2.1 Theoretical Background 8
2.2 System Model 12
2.2.1 Angle Estimation via 2D MUSIC Algorithm 17
2.3 Conventional Spatial Detection Algorithms: CFAR and DBSCAN 18
2.4 Proposed Method: Spatiotemporal Detection Algorithm with Angular Constraints 21
2.4.1 Concept and Motivation 21
2.4.2 3D-DCNN Architecture for Spatiotemporal Filtering 24
2.5 Experimental Environment and Scenario 26
2.5.1 Hardware: The TORIS AESA Radar System 27
2.5.2 Field Test Scenario 28
2.6 Experiment Results 29
2.6.1 Performance Evaluation Metrics 29
2.6.2 Experiment Result 30
2.7 Chapter Summary 34
3. Radio Frequency Signal Scanner: Long-Range Detection 36
3.1 Theoretical Background 36
3.2 System Model 39
3.3 Detection and DOA Estimation using MUSIC 41
3.3.1 MUSIC algorithm 41
3.4 Hardware Implementation 43
3.4.1 Antenna Array Design 44
3.4.2 RF Module and Signal Acquisition 47
3.5 Experimental Setup 48
3.6 Experimental Results and Discussion 49
3.6.1 Detection Performance Results 49
3.6.2 Theoretical Analysis of Multipath-Induced Elevation Error 52
3.7 Chapter Summary 57
4. Infrared Camera: Motion-Based Bird-Drone Classification for Long- Range Surveillance 59
4.1 Theoretical Background 59
4.2 System Model 61
4.3 Proposed Spatiotemporal Identification Algorithm 64
4.3.1 Spatio-Temporal Classification using Fourier Descriptors 67
4.3.2 CNN Architecture for Motion Pattern Classification 71
4.3.3 Appearance-Based Classification using 2D-CNN 72
4.4 Analysis of Classification Results 73
4.5 Chapter Summary 76
5. Jammer: Robust Directional Jamming Strategies Against UAV Location Inaccuracy 78
5.1 System Model 80
5.2 Radar Detection Process 81
5.2.1 Radar System Model 81
5.2.2 Radar based UAV Detection Process 83
5.3 Conventional Beam Control Schemes 84
5.3.1 Directional Antenna-based Beam Control Scheme (DA) 85
5.3.2 Beamforming based Beam Control Scheme (Steering) 87
5.4 Beam Control Scheme Considering Location Information Inaccuracy 88
5.4.1 Jamming Region Determination 89
5.4.2 Proposed Beam Control Scheme with Adaptive Beamwidth 90
5.4.3 Woodward-Lawson Method (WL) 92
5.4.4 Fourier Transform Method 93
5.4.5 Parks-McClellan Algorithm (PM) 94
5.4.6 Random Beam Control Scheme with Conventional Method 97
5.5 Performance Evaluation 98
5.6 Chapter Summary 108
6. Conclusion 110
6.1 Summary of the Dissertation 110
6.2 Contributions and Significance 111
6.3 Limitations and Future Work 112
References 114

URI

https://scholar.dgist.ac.kr/handle/20.500.11750/59625
http://dgist.dcollection.net/common/orgView/200000943818

DOI

10.22677/THESIS.200000943818

Degree

Doctor

Department

Department of Electrical Engineering and Computer Science

Publisher

DGIST

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