GNSS jamming detection using attention-based mutual information feature selection
Tóm tắt
Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal weakness, GNSS is susceptible to interference, fading, and jamming, which reduces the position accuracy. Therefore, it would be beneficial to have a simple and highly accurate model for detecting the jamming signals to improve the GNSS receiver accuracy. In this paper, we propose a hybrid deep learning (DL) model for predicting jamming signals. Initially, we utilize a feature selection algorithm that combines mutual information (MI) with the minimal redundancy maximum relevance (mRMR) technique to identify the most crucial features. Subsequently, the model undergoes training using a soft attention-double-layer bidirectional long short-term memory (A-DBiLSTM) model. This particular model has shown outstanding performance in comparison to other DL models when applied to datasets collected from both kinematic and static jamming scenarios. To assess the effectiveness and efficiency of the proposed MI feature selection algorithm, we evaluate its performance through the calculation of confusion matrices and conducting numerical simulations. The simulation results of the A-DBiLSTM model demonstrate higher accuracy, precision, recall, and
$$\mathrm {F1_{Score}}$$
of
$$98.82\%$$
,
$$98.4\%$$
,
$$98.68\%$$
, and
$$98.36\%$$
, respectively. By employing the MI feature selection algorithm, dimensionality reduction is achieved. Moreover, the MI feature selection algorithm reduces
$$19\%$$
of the learning time with almost the same accuracy.
Từ khóa
Tài liệu tham khảo
Dovis F. GNSS interference threats and countermeasures. London: Artech House; 2015.
Mitch RH, Dougherty RC, Psiaki ML, Powell SP, O’Hanlon BW, Bhatti JA, Humphreys TE. Signal characteristics of civil gps jammers. In: Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011); 2011. p. 1907–1919.
Sun K, Chen Y. A novel GNSS sweep interference detection and mitigation method based on Radon–Wigner transform. IEEE Sens J. 2023. https://doi.org/10.1109/JSEN.2023.3240429.
Elmezayen A, Karaim M, Elghamrawy H, Noureldin A. Enhanced GNSS reliability on high-dynamic platforms: a comparative study of multi-frequency, multi-constellation signals in jamming environments. Sensors. 2023;23(23):9552.
Hu Y, Bian S, Li B, Zhou L. A novel array-based spoofing and jamming suppression method for GNSS receiver. IEEE Sens J. 2018;18(7):2952–8.
Jang J, Seo S, Ahn W-G, Lee J, Park J. Performance analysis of an interference cancellation technique for radio navigation. IET Radar Sonar Navigation. 2018;12(4):426–32.
Borio D, Camoriano L, Savasta S, Presti LL. Time-frequency excision for GNSS applications. IEEE Syst J. 2008;2(1):27–37. https://doi.org/10.1109/JSYST.2007.914914.
Sun K, Jin T, Yang D. A new reassigned spectrogram method in interference detection for GNSS receivers. Sensors. 2015;15(9):22167–91. https://doi.org/10.3390/s150922167.
Sun K, Zhang M, Yang D. A new interference detection method based on joint hybrid time-frequency distribution for GNSS receivers. IEEE Trans Vehicle Tech. 2016;65(11):9057–71. https://doi.org/10.1109/TVT.2016.2515718.
Sharifi-Tehrani O, Sabahi MF. Eigen analysis of flipped Toeplitz covariance matrix for very low snr sinusoidal signals detection and estimation. Digital Signal Process. 2022;129:103677. https://doi.org/10.1016/j.dsp.2022.103677.
Sharifi-Tehrani O, Sabahi MF, Raees Danaee M. Efficient GNSS jamming mitigation using the Marcenko Pastur law and Karhunen–Loeve decomposition. IEEE Trans Aerosp Electron Syst. 2022;58(3):2291–303. https://doi.org/10.1109/TAES.2021.3131400.
Na H, Shin Y, Lee D, Lee J. LSTM-based throughput prediction for LTE networks. ICT Express; 2021.
Mao Q, Hu F, Hao Q. Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Commun Surv Tuts. 2018;20(4):2595–621. https://doi.org/10.1109/COMST.2018.2846401.
Rauber TW, de Assis Boldt F, Varejão FM. Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans Ind Electron. 2014;62(1):637–46.
Li Y, Pawlak J, Price J, Al Shamaileh K, Niyaz Q, Paheding S, Devabhaktuni V. Jamming detection and classification in OFDM-based UAVs via feature-and Spectrogram-Tailored machine learning. IEEE Access. 2022;10:16859–70.
Liu X, Xu Y, Jia L, Wu Q, Anpalagan A. Anti-jamming communications using spectrum waterfall: a deep reinforcement learning approach. IEEE Commun Lett. 2018;22(5):998–1001.
Czech D, Mishra A, Inggs M. A CNN and LSTM-based approach to classifying transient radio frequency interference. Astron Comput. 2018;25:52–7.
Xiao N, Song Z. Signal interference detection algorithm based on Bidirectional Long Short-Term Memory neural network. Math Prob Eng. 2022;22.
Zhou Y, Zhang X, Ding F. Hierarchical estimation approach for RBF-AR models with regression weights based on the increasing data length. IEEE Trans Circuits Syst II Express Briefs. 2021;68(12):3597–601.
Wang Y, Ding F. Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model. Automatica. 2016;71:308–13.
Ruiz AP, Flynn M, Large J, Middlehurst M, Bagnall A. The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc. 2021;35(2):401–49.
Lines J, Bagnall A. Time series classification with ensembles of elastic distance measures. Data Min Knowl Disc. 2015;29:565–92.
Xu Z, King I, Lyu MR-T, Jin R. Discriminative semi-supervised feature selection via manifold regularization. IEEE Trans Neural Netw. 2010;21(7):1033–47.
Vasconcelos M, Vasconcelos N. Natural image statistics and low-complexity feature selection. IEEE Trans Pattern Anal Mach Intell. 2008;31(2):228–44.
Shen L, Bai L. Information theory for Gabor feature selection for face recognition. EURASIP J Adv Signal Process. 2006;2006:1–11.
Xue B, Zhang M, Browne WN, Yao X. A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput. 2016;20(4):606–26. https://doi.org/10.1109/TEVC.2015.2504420.
Liu Y, Mu Y, Chen K, Li Y, Guo J. Daily activity feature selection in smart homes based on Pearson correlation coefficient. Neural Process Lett. 2020;51(2):1771–87.
Wesson KD, Gross JN, Humphreys TE, Evans BL. GNSS signal authentication via power and distortion monitoring. IEEE Trans Aerosp Electron Syst. 2018;54(2):739–54. https://doi.org/10.1109/TAES.2017.2765258.
Qin W, Dovis F. Situational awareness of chirp jamming threats to GNSS based on supervised machine learning. IEEE Trans Aerosp Electron Syst. 2021;58(3):1707–20.
Ultahack, European GNSS Agency (GSA), and Ublox: Galileo Innovation Challenge (2019). https://ultrahack.org/galileoinnovationchallenge. Accessed Nov 2020
Gurtner W, Estey L. Rinex-the receiver independent exchange format-version 3.00. Astronomical Institute, University of Bern and UNAVCO, Bolulder, Colorado. 2007.
Emeç M, Özcanhan MH. A hybrid deep learning approach for intrusion detection in IoT networks. Adv Electr Comput Eng. 2022;22(1):3–12.
Kshirsagar D, Kumar S. An efficient feature reduction method for the detection of dos attack. ICT Express. 2021;7(3):371–5.
Bennasar M, Hicks Y, Setchi R. Feature selection using joint mutual information maximization. Expert Syst Appl. 2015;42(22):8520–32.
Wang Y, Cang S, Yu H. Mutual information inspired feature selection using kernel canonical correlation analysis. Expert Syst Appl: X. 2019;4:100014.
Yu Y, Si X, Hu C, Zhang J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 2019;31(7):1235–70.
Liu G, Guo J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing. 2019;337:325–38.
Zhang J. Machine learning with feature selection using principal component analysis for malware detection: a case study; 2019. arXiv:1902.03639