LANG Rongling, SU Zhen, ZHOU Kai, MOU Shouxian. A Robust Signal Driven Method for GNSS Signals Interference Detection[J]. Chinese Journal of Electronics, 2018, 27(2): 422-427. doi: 10.1049/cje.2018.01.018
Citation: LANG Rongling, SU Zhen, ZHOU Kai, MOU Shouxian. A Robust Signal Driven Method for GNSS Signals Interference Detection[J]. Chinese Journal of Electronics, 2018, 27(2): 422-427. doi: 10.1049/cje.2018.01.018

A Robust Signal Driven Method for GNSS Signals Interference Detection

doi: 10.1049/cje.2018.01.018
Funds:  This work is supported by the National Natural Science Foundation of China (No.61202078).
  • Received Date: 2015-09-29
  • Rev Recd Date: 2016-03-16
  • Publish Date: 2018-03-10
  • Interference can severely degrade the performance of the Global navigation satellite system (GNSS) receivers. Therefore it is important to detect the interference accurately and efficiently. Both the pre-correlation method and post-correlation method currently in use require certain strict pre-conditions, which limit their application. A new pre-correlation method that could be applied in most cases, called GNSS signal driven (GSD) method is proposed. The essence of the GSD method is to use classification techniques to detect the interference, based on the feature parameters extracted directly from the GNSS signals. The Support vector machine (SVM) and the Competitive agglomeration (CA) are adopted as the classification algorithms. When a classifier can be trained in advance, the SVM method is used, otherwise the CA method is adopted. Both methods show satisfying detection accuracy, especially the SVM method, whereas the robust CA method has an even wider application. The effectiveness of the proposed method is verified properly by experiments with the GPS L1 band Coarse/acquisition (C/A) signals.
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  • S. W. Gilmore and W. Delaney, "Jamming of GPS receivers:A stylized analysis", Project Report, Lincoln Laboratory, 1994.
    A.T. Balaei and A.G. Dempster, "A statistical interference technique for GPS interference detection", IEEE Transactions on Aerospace and Electronic, Vol.45, No.4, pp.1499-1511, 2009.
    P. Ndili and A. Enge, "GPS receiver autonomous interference detection", IEEE Position Location Nav. Symp., Vol.23, No.3, pp.123-130, 1988.
    P. T. Capozza, B. J. Holland, T. M. Hopkinson, et al., "Measured effects of a narrowband interference suppressor on GPS receivers", Proc. of Proceedings of Annual Meeting of The Institute of Navigation, Cambridge, MA, USA, pp.645-651, 1999.
    L. Marti and F. van Graas, "Bias detection and its confidence assessment in global positioning system signals", IEEE Aerospace Conference, Vol.3, pp.1608-1617, 2004.
    A. Tani and R. Fantacci, "Performance evaluation of a precorrelation interference detection algorithm for the GNSS based on nonparametrical spectral estimation", IEEE Systems Journal, Vol.2, No.1, pp. 20-26, 2008.
    D. Borio, L. Camoriano, S. Savasta, et al., "Time-frequency excision for GNSS applications", IEEE Systems Journal, Vol.2, No.1, pp.27-37, 2008.
    F. Faurie and A. Giremus, "Bayesian detection of interference in satellite navigation systems", Acoustics, IEEE International Conference on Speech and Signal Processing, pp.4348-4351, 2011.
    N.Y. Deng and Y.J. Tian, A New Method of Data Mining:Support Vector Machine, Science Press, Beijing, China, 2004.
    B. Baesens, Vanthienen and J. Baesens, "Benchmarking stateof-the-art classification algorithms for credit scoring", Journal of the Operational Research Society, Vol.54, No.6, pp.627-635, 2003.
    H. Frigui and R. Krishnapuram, "Clustering by competitive agglomeration", Pattern Recognition, Vol.30, No.7, pp.1109-1119, 1997.
    R.L. Lang and X.L. Deng, "The heuristic algorithms for selecting the parameters of support vector machine for classification", Chinese Journal of Electronics, Vol.21, No.3, pp.485-488, 2012.
    H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applications in computer vision", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.21, No.5, pp.450-465, 1999.
    F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, et al., Robust Statistics:The Approach Based on Influence Functions, John Wiley & Sons, NewYork, USA, 1986.
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