지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.10
- 수록면
- 950 - 961 (12page)
- DOI
- 10.5302/J.ICROS.2022.22.0135
이용수
초록· 키워드
오류제보하기
In this paper, a transfer learning-based global navigation satellite system (GNSS) jamming classification scheme using overlapped images of the smoothed pseudo-Wigner-Ville distribution (SPWVD) and power spectral density (PSD) is proposed. Five types of jamming are considered: amplitude modulation jamming (continuous wave interference), linear chirp jamming, frequency modulation jamming, narrow-band noise jamming, and distance measurement equipment jamming (pulse jamming). The performance of the proposed scheme is shown to be the F1 score and the confusion matrix for pre-mentioned 5 jamming types with no jamming case. A representative conventional transfer learning-based GNSS jamming classification scheme uses a concatenated image comprising a spectrogram, in-phase and quadrature constellation, PSD, and histogram. However, in conventional schemes, the spectrogram"s time-frequency (TF) resolution is low and image quality is reduced by concatenating the images. In comparison, the proposed scheme uses SPWVD with high TF resolution and there is no degradation of image quality caused by overlapping. Through cross-validation and testing using the same dataset, the average classification accuracy of the conventional scheme using supported vector machine (SVM), logistics regression (LR), and random forest (RF) were 97.7%, 97.5%, 96.5%, respectively, compared to 98.8%, 98.5%, and 98.0%, respectively, for the proposed scheme. The average classification accuracy of the proposed scheme was approximately 1.1%p, 1.0%p, and 1.5%p better than that of the conventional scheme for SVM, LR, and RF, respectively.
목차
Abstract
I. 서론
II. 신호 모형과 시간-주파수 분석 기법
III. 결합 이미지를 사용한 전이학습 기반 재밍식별기법
IV. 제안하는 기법과 성능 평가
V. 결론
REFERENCES
참고문헌 (20)
참고문헌 신청
Ha-J. Kim and K.-S. Kim, “Lightweight real-time object tracking based on pixel and GPS for mobile surveillance platform,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 12, pp. 991-999, Dec. 2021.
K. Ryu, G. Park, Y. J. Lee, and S. Sung, “Real-time performance improvement of GNSS navigation in urban cannon via 3D high-speed computation engine and 3D map information,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 11, pp. 824-829, Nov. 2021.
R. T. Ioannides, T. Pany, and G. Gibbons, “Known vulnerabilities of global navigation satellite systems, status, and potential mitigation techniques,” Proceedings of the IEEE, vol. 104, no. 4, pp. 1174-1194, Jun. 2016.
T. Kraus, R. Bauernfeind, and B. Eissfeller, “Survey of in-car jammers - Analysis and modeling of the RF signals and IF samples (Suitable for active signal cancellation),” Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), pp. 430-435, Portland, OR, Sep. 2011.
K. J. S. Lorraine and M. Ramarakula, “A comprehensive survey on GNSS interferences and the application of neural networks for anti-jamming,” IETE Journal of Research, Review Article, pp. 1-20, Aug. 2021.
K. Ryu, G. Park, Y. J. Lee, and S. Sung, “Real-time performance improvement of GNSS navigation in urban cannon via 3D high-speed computation engine and 3D map information,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 11, pp. 824-829, Nov. 2021.
R. T. Ioannides, T. Pany, and G. Gibbons, “Known vulnerabilities of global navigation satellite systems, status, and potential mitigation techniques,” Proceedings of the IEEE, vol. 104, no. 4, pp. 1174-1194, Jun. 2016.
T. Kraus, R. Bauernfeind, and B. Eissfeller, “Survey of in-car jammers - Analysis and modeling of the RF signals and IF samples (Suitable for active signal cancellation),” Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), pp. 430-435, Portland, OR, Sep. 2011.
K. J. S. Lorraine and M. Ramarakula, “A comprehensive survey on GNSS interferences and the application of neural networks for anti-jamming,” IETE Journal of Research, Review Article, pp. 1-20, Aug. 2021.
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