A Robust Signal Driven Method for GNSS Signals Interference Detection

LANG Rongling; SU Zhen; ZHOU Kai; MOU Shouxian

doi:10.1049/cje.2018.01.018

Volume 27 Issue 2

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Article Navigation > Chinese Journal of Electronics > 2018 > 27(2): 422-427

LANG Rongling, SU Zhen, ZHOU Kai, et al., “A Robust Signal Driven Method for GNSS Signals Interference Detection,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 422-427, 2018, doi: 10.1049/cje.2018.01.018

Citation:

LANG Rongling, SU Zhen, ZHOU Kai, et al., “A Robust Signal Driven Method for GNSS Signals Interference Detection,” Chinese Journal of Electronics, vol. 27, no. 2, pp. 422-427, 2018, doi: 10.1049/cje.2018.01.018

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A Robust Signal Driven Method for GNSS Signals Interference Detection

doi: 10.1049/cje.2018.01.018

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;
2.
Institute of Beijing Electromechanical Engineering, Beijing 100074, China;
3.
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 610200, China;
4.
School of Physical Electronics, University of Electronic Science and Technology, Chengdu 611731, China

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

Abstract

Abstract

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.
- GNSS,
- CA,
- Interference detection,
- SVM

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References(14)

References

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