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Detection of frequency-hopping signals under the blind condition

Detection of frequency-hopping signals under the blind condition

자료유형
학위논문
개인저자
이경규, 李炅奎
서명 / 저자사항
Detection of frequency-hopping signals under the blind condition / Kyung-gyu Lee
발행사항
Seoul :   Graduate School, Korea University,   2020  
형태사항
iv, 35장 : 도표 ; 26 cm
기타형태 저록
Detection of frequency-hopping signals under the blind condition   (DCOLL211009)000000232143  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2020. 8
학과코드
0510   6YD36   381  
일반주기
지도교수: 오성준  
서지주기
참고문헌: 장 33-35
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PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Detection , Deep learning , Uncooperative condition , Blind condition , Frequency-hopping signals,,
000 00000nam c2200205 c 4500
001 000046048377
005 20200918174530
007 ta
008 200629s2020 ulkd bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6YD36 ▼c 381
100 1 ▼a 이경규, ▼g 李炅奎
245 1 0 ▼a Detection of frequency-hopping signals under the blind condition / ▼d Kyung-gyu Lee
246 1 1 ▼a 암맹 환경에서의 주파수 도약 신호 탐지
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020
300 ▼a iv, 35장 : ▼b 도표 ; ▼c 26 cm
500 ▼a 지도교수: 오성준
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2020. 8
504 ▼a 참고문헌: 장 33-35
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Detection ▼a Deep learning ▼a Uncooperative condition ▼a Blind condition ▼a Frequency-hopping signals
776 0 ▼t Detection of frequency-hopping signals under the blind condition ▼w (DCOLL211009)000000232143
900 1 0 ▼a Lee, Kyung-gyu, ▼e
900 1 0 ▼a 오성준, ▼g 俉誠埈, ▼e 지도교수
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
Detection of frequency-hopping signals under the blind condition (13회 열람)
PDF 초록 목차

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 381 등록번호 123064848 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6YD36 381 등록번호 123064849 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

초록

The frequency-hopping (FH) technique is widely used in commercial and
military communications for its superiority of
anti-jamming and low probability of interception/detection capabilities.
In order to achieve such superiority, a FH signal randomly changes
its carrier frequency within a transmission symbol.
Detection of the FH signal is challenging when the hopping period or frequency offset are unknown.
May researchers proposed lots of detection schemes, but they showed low performance when parameters are unknown or blind condition.
In order to detect the signal of FH system, we propose detection schemes under the blind condition in this dissertation.

In the first part of the dissertation, a Dirty-Template-based detection scheme is proposed.
Dirty-Template was originally proposed to blindly estimate
the time delay in an ultra-wide-band system.
We propose that it can be employed in the frequency domain
to detect fast frequency-hopping signal.
The decision statistic derived from the cross-correlation between the template and
received signals in the frequency domain is analyzed.
Using this cross-correlation, a detection scheme based on the Neyman-Pearson test is proposed.
Through simulations, the receiver operating characteristics of the Dirty-Template-based and autocorrelation-based detection schemes are compared.
The simulation results show that the Dirty-Template-based scheme outperforms the autocorrelation-based scheme when the hopping period is short.


In the second part of the dissertation, a detection scheme of FH signals using the deep learning is proposed.
Conventional spectrogram-based schemes can detect FH signals, but its performance is limited by the time-frequency resolution trade-off and spectral leakage.
To alleviate this issue, we design convolutional neural network~(CNN) and hybrid CNN/recurrent neural network~(RNN)-based schemes.
The CNN-based scheme alleviates spectral leakage by using feature maps.
The hybrid CNN/RNN-based scheme mitigates the time-frequency resolution trade-off by using feature maps extracted from spectrograms of various window lengths.
Through simulations, the accuracy and receiver operating characteristics area under cover of the deep learning-based and conventional detection schemes are compared.
The hybrid CNN/RNN-based scheme is shown to outperform the CNN-based and conventional detection schemes.

목차

Abstract
Contents i
List of Figures iii
List of Tables iv
1 Introduction 1
2 Detection of Fast Frequency-Hopping Signals Using Dirty Template in
the Frequency Domain 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 System Assumption and Cross-Correlation Computation . . . . . . . . . . 7
2.3 Dirty-Template in the Frequency Domain . . . . . . . . . . . . . . . . . . 9
2.3.1 Mean and Variance of Cross Correlation . . . . . . . . . . . . . . . 9
2.3.2 Neyman-Pearson Test . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.3 Acquisition of the Template . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.2 Computational Complexity Analysis . . . . . . . . . . . . . . . . . 15
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
i
3 Detection of Frequency-Hopping Signals with Deep Learning 18
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 CNN-based and Hybrid CNN-RNN-based scheme . . . . . . . . . . . . . . 21
3.3.1 CNN-based scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.2 Hybrid CNN and RNN-based scheme . . . . . . . . . . . . . . . . 22
3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4.1 Generation of FH signal samples . . . . . . . . . . . . . . . . . . . 24
3.4.2 Implementation details of detection schemes . . . . . . . . . . . . . 24
3.4.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4 Conclusion 31
Bibliography 33