Frekans Atlamalı Sinyallerin Tespiti ve Parametre Kestirimine Genel Bir Bakış: Son Gelişmeler ve Zorluklar

Yıl 2024, Cilt: 14 Sayı: 2, 99 – 105, 30.07.2024

Mutlu Aydın Ali Kara

Öz

Frequency hopping spread spectrum (FHSS) or simply frequency hopping (FA) is a common communication method that changes the carrier frequency. Frequency hopping communication method is widely used for secure communication link due to its various benefits such as strong anti-jamming ability. As systems using frequency hopping increase, the need for detection of frequency hopping signals and parameter estimation has increased, and the development of these methods has become a critical research area in terms of security. In this study, recent studies in the field of detection and parameter estimation of frequency hopping signals are examined in two separate categories for single and multiple targets. Additionally, future research areas and challenges are discussed. Thus, future studies are guided.

Anahtar Kelimeler

Frequency Hopping, FHSS, Detection, Parameter Estimation

Kaynakça

• [1] D. Torrieri, Principles of Spread-Spectrum communication systems. Springer, 2018.
• [2] V. V. D. Knaap, M. Mouri, ve P. Zwamborn, “MSG-SET-183 – Detection and Characterization of a UAS RF FHSS Communication Link.,” NATO S&T Organization, 2021.
• [3] B. Kaplan, I. Kahraman, A. Gorcin, H. A. Cirpan, ve A. R. Ekti, “Measurement based FHSS–type drone controller detection at 2.4GHz: An STFT approach,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020.
• [4] P. Flak, “Drone detection sensor with continuous 2.4 ghz ISM band coverage based on cost-effective SDR platform,” IEEE Access, vol. 9, pp. 114574–114586, 2021.
• [5] D. Mototolea, R. Youssef, E. Radoi, ve I. Nicolaescu, “Non-cooperative low-complexity detection approach for FHSS-GFSK drone control signals,” IEEE Open Journal of the Communications Society, vol. 1, pp. 401–412, 2020.
• [6] J. Ye, “A new frequency hopping signal detection of civil UAV based on improved K-means clustering algorithm,” IEEE Access, vol. 9, pp. 53190–53204, 2021.
• [7] S. Basak, S. Rajendran, S. Pollin, ve B. Scheers, “Combined RF-based drone detection and classification,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 111–120, 2022.
• [8] M. T. Khan, A. Z. Sha’ameri, ve M. M. Zabidi, “Classification of FHSS signals in a multi-signal environment by Artificial Neural Network,” International Journal of Computing and Digital Systems, vol. 11, no. 1, pp. 775–789, 2022.
• [9] Z. Deng, ve J. Lei, “Spectrogram-based frequency hopping signal detection in a complex electromagnetic environment,” 2022 7th International Conference on Signal and Image Processing (ICSIP), 2022.
• [10] Z. Chen, “Unlocking signal processing with image detection: A frequency hopping detection scheme for complex EMI environments using STFT and CenterNet,” IEEE Access, vol. 11, pp. 46004–46014, 2023.
• [11] H. Zhu, H. Lv, Z. Dai, M. Tan, ve W. Song, “A novel parameter estimation method of fhss signal with low snr,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 18, no. 6, pp. 891–900, 2023.
• [12] L. Zhi, Z. Jianhua, C. Hao, G. Xu, ve L. Jian, “Parameter estimation of frequency hopping signals based on analogue information converter,” IET Communications, vol. 13, no. 13, pp. 1886–1892, 2019. [13] Y. He, Y. Su, Y. Chen, Y. Yu, ve X. Yang, “Double window spectrogram difference method: A blind estimation of frequency-hopping signal for battlefield communication environment,” 2018 24th Asia-Pacific Conference on Communications (APCC), 2018.
• [14] Md. Z. Hasan, D. J. Couto, M. A. Abdel-Malek, ve J. H. Reed, “Frequency hopping signal detection in low signal-to-noise ratio regimes,” 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2023.
• [15] A. Kanaa ve A. Z. Sha’ameri, “A robust parameter estimation of FHSS signals using time–frequency analysis in a non-cooperative environment,” Physical Communication, vol. 26, pp. 9–20, 2018.
• [16] K.-G. Lee ve S.-J. Oh, Detection of Frequency-Hopping signals with deep learning. IEEE Communications Letters, 24(5), 1042–1046, 2020.
• [17] C. Li, Y. Chen, ve Z. Zhao, “Frequency hopping signal detection based on optimized generalized S transform and ResNet,” Mathematical Biosciences and Engineering, vol. 20, no. 7, pp. 12843–12863, 2023.
• [18] Y. Wang, H. Liao, S. Yuan, ve N. Liu, “A Learning-Based signal parameter extraction approach for Multi-Source Frequency-Hopping signal sorting,” IEEE Signal Processing Letters, vol. 30, pp. 1162–1166, 2023.
• [19] Z. Wang, B. Zhang, Z. Zhu, Z. Wang, ve K. Gong, “Signal sorting algorithm of hybrid frequency hopping network station based on neural network,” IEEE Access, vol. 9, pp. 35924–35931, 2021.
• [20] D. Zhang, Y. Shang, X. Liang, ve J. Lin, “Efficient blind estimation of parameters for multiple frequency hopping signals via single channel,” 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 2022
• [21] K. Lu, Z. Qian, M. Wang, ve D. Wang, “Few-shot learning based blind parameter estimation for multiple frequency-hopping signals,” Multidimensional Systems and Signal Processing, vol. 34, no. 1, pp. 271–289, 2023.
• [22] Y. Li, F. Wang, G. Fan, Y. Liu, ve Y. Zhang, “A fast estimation algorithm for parameters of multiple Frequency-Hopping signals based on compressed spectrum sensing and maximum likelihood,” Electronics, vol. 12, no. 8, p. 1808, 2023.
• [23] Y. Wang, “Detection and parameter estimation of frequency hopping signal based on the deep neural network,” International Journal of Electronics, vol. 109, no. 3, pp. 520–536, 2021.
• [24] J. Wan, D. Zhang, W. Xu, ve Q. Guo, “Parameter estimation of multi frequency hopping signals based on space-time-frequency distribution,” Symmetry, vol. 11, no. 5, p. 648, May 2019.
• [25] M. Lin, Y. Tian, X. Zhang, ve Y. Huang, “Parameter estimation of Frequency-Hopping signal in UCA based on deep learning and spatial Time–Frequency distribution,” IEEE Sensors Journal, vol. 23, no. 7, pp. 7460–7474, 2023.

Frekans Atlamalı Sinyallerin Tespiti ve Parametre Kestirimine Genel Bir Bakış: Son Gelişmeler ve Zorluklar

Yıl 2024, Cilt: 14 Sayı: 2, 99 – 105, 30.07.2024

Mutlu Aydın Ali Kara

Öz

Frekans atlamalı yayılı spektrum (İng. Frequency Hopping Spread Spectrum (FHSS)) ya da kısaca frekans atlamalı (FA), taşıyıcı frekansı değiştiren yaygın bir haberleşme yöntemidir. Frekans atlamalı haberleşme yöntemi, anti-karıştırma kabiliyetinin güçlü olması gibi çeşitli yararlarından dolayı güvenli iletişim bağlantısı için yaygın olarak kullanılmaktadır. Frekans atlama kullanan sistemler arttıkça, frekans atlayan sinyallerinin tespiti ve parametre kestirimine olan ihtiyaç daha da artmış ve bu yöntemlerin geliştirilmesi, güvenlik açısından kritik bir araştırma alanı haline gelmiştir. Bu çalışmada frekans atlamalı sinyalleri tespit ve parametre kestirimi alanındaki son çalışmalar tek ve çoklu hedefler için iki ayrı kategoride incelenmiştir. Ayrıca gelecek araştırma alanları ve zorlukları tartışılmıştır. Böylece gelecekteki çalışmalara yol gösterilmiştir.

Anahtar Kelimeler

Frekans Atlamalı, FHSS, Tespit, Parametre Kestirimi

Kaynakça

• [1] D. Torrieri, Principles of Spread-Spectrum communication systems. Springer, 2018.
• [2] V. V. D. Knaap, M. Mouri, ve P. Zwamborn, “MSG-SET-183 – Detection and Characterization of a UAS RF FHSS Communication Link.,” NATO S&T Organization, 2021.
• [3] B. Kaplan, I. Kahraman, A. Gorcin, H. A. Cirpan, ve A. R. Ekti, “Measurement based FHSS–type drone controller detection at 2.4GHz: An STFT approach,” 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020.
• [4] P. Flak, “Drone detection sensor with continuous 2.4 ghz ISM band coverage based on cost-effective SDR platform,” IEEE Access, vol. 9, pp. 114574–114586, 2021.
• [5] D. Mototolea, R. Youssef, E. Radoi, ve I. Nicolaescu, “Non-cooperative low-complexity detection approach for FHSS-GFSK drone control signals,” IEEE Open Journal of the Communications Society, vol. 1, pp. 401–412, 2020.
• [6] J. Ye, “A new frequency hopping signal detection of civil UAV based on improved K-means clustering algorithm,” IEEE Access, vol. 9, pp. 53190–53204, 2021.
• [7] S. Basak, S. Rajendran, S. Pollin, ve B. Scheers, “Combined RF-based drone detection and classification,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 111–120, 2022.
• [8] M. T. Khan, A. Z. Sha’ameri, ve M. M. Zabidi, “Classification of FHSS signals in a multi-signal environment by Artificial Neural Network,” International Journal of Computing and Digital Systems, vol. 11, no. 1, pp. 775–789, 2022.
• [9] Z. Deng, ve J. Lei, “Spectrogram-based frequency hopping signal detection in a complex electromagnetic environment,” 2022 7th International Conference on Signal and Image Processing (ICSIP), 2022.
• [10] Z. Chen, “Unlocking signal processing with image detection: A frequency hopping detection scheme for complex EMI environments using STFT and CenterNet,” IEEE Access, vol. 11, pp. 46004–46014, 2023.
• [11] H. Zhu, H. Lv, Z. Dai, M. Tan, ve W. Song, “A novel parameter estimation method of fhss signal with low snr,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 18, no. 6, pp. 891–900, 2023.
• [12] L. Zhi, Z. Jianhua, C. Hao, G. Xu, ve L. Jian, “Parameter estimation of frequency hopping signals based on analogue information converter,” IET Communications, vol. 13, no. 13, pp. 1886–1892, 2019. [13] Y. He, Y. Su, Y. Chen, Y. Yu, ve X. Yang, “Double window spectrogram difference method: A blind estimation of frequency-hopping signal for battlefield communication environment,” 2018 24th Asia-Pacific Conference on Communications (APCC), 2018.
• [14] Md. Z. Hasan, D. J. Couto, M. A. Abdel-Malek, ve J. H. Reed, “Frequency hopping signal detection in low signal-to-noise ratio regimes,” 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2023.
• [15] A. Kanaa ve A. Z. Sha’ameri, “A robust parameter estimation of FHSS signals using time–frequency analysis in a non-cooperative environment,” Physical Communication, vol. 26, pp. 9–20, 2018.
• [16] K.-G. Lee ve S.-J. Oh, Detection of Frequency-Hopping signals with deep learning. IEEE Communications Letters, 24(5), 1042–1046, 2020.
• [17] C. Li, Y. Chen, ve Z. Zhao, “Frequency hopping signal detection based on optimized generalized S transform and ResNet,” Mathematical Biosciences and Engineering, vol. 20, no. 7, pp. 12843–12863, 2023.
• [18] Y. Wang, H. Liao, S. Yuan, ve N. Liu, “A Learning-Based signal parameter extraction approach for Multi-Source Frequency-Hopping signal sorting,” IEEE Signal Processing Letters, vol. 30, pp. 1162–1166, 2023.
• [19] Z. Wang, B. Zhang, Z. Zhu, Z. Wang, ve K. Gong, “Signal sorting algorithm of hybrid frequency hopping network station based on neural network,” IEEE Access, vol. 9, pp. 35924–35931, 2021.
• [20] D. Zhang, Y. Shang, X. Liang, ve J. Lin, “Efficient blind estimation of parameters for multiple frequency hopping signals via single channel,” 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 2022
• [21] K. Lu, Z. Qian, M. Wang, ve D. Wang, “Few-shot learning based blind parameter estimation for multiple frequency-hopping signals,” Multidimensional Systems and Signal Processing, vol. 34, no. 1, pp. 271–289, 2023.
• [22] Y. Li, F. Wang, G. Fan, Y. Liu, ve Y. Zhang, “A fast estimation algorithm for parameters of multiple Frequency-Hopping signals based on compressed spectrum sensing and maximum likelihood,” Electronics, vol. 12, no. 8, p. 1808, 2023.
• [23] Y. Wang, “Detection and parameter estimation of frequency hopping signal based on the deep neural network,” International Journal of Electronics, vol. 109, no. 3, pp. 520–536, 2021.
• [24] J. Wan, D. Zhang, W. Xu, ve Q. Guo, “Parameter estimation of multi frequency hopping signals based on space-time-frequency distribution,” Symmetry, vol. 11, no. 5, p. 648, May 2019.
• [25] M. Lin, Y. Tian, X. Zhang, ve Y. Huang, “Parameter estimation of Frequency-Hopping signal in UCA based on deep learning and spatial Time–Frequency distribution,” IEEE Sensors Journal, vol. 23, no. 7, pp. 7460–7474, 2023.

Toplam 24 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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