GNSS World of China

Volume 49 Issue 4
Aug.  2024
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ZHAO Shen, LIAO Yifei, LI Shiling, ZHOU Kaijun. Research on GNSS spoofing interference detection for multi-correlator combined power[J]. GNSS World of China, 2024, 49(4): 66-74. doi: 10.12265/j.gnss.2023235
Citation: ZHAO Shen, LIAO Yifei, LI Shiling, ZHOU Kaijun. Research on GNSS spoofing interference detection for multi-correlator combined power[J]. GNSS World of China, 2024, 49(4): 66-74. doi: 10.12265/j.gnss.2023235

Research on GNSS spoofing interference detection for multi-correlator combined power

doi: 10.12265/j.gnss.2023235
  • Received Date: 2023-12-25
  • Accepted Date: 2023-12-25
  • Available Online: 2024-07-05
  • Global Navigation Satellite System (GNSS) civil signals are vulnerable to external spoofing because of their openness and vulnerability. As an effective method for spoofing detection, Signal Quality Monitoring (SQM) monitors the correlation results of early code, late code and phase code (ELP) after the receiver's tracking loop, and compares them with the correlation characteristics without spoofing to determine whether spoofing interference exists. The conventional SQM algorithm uses only three ELP information and the detection performance is limited. Therefore, a multi-correlator combined power algorithm is proposed. The weight of the output power of multiple equally spaced correlators between ELP is taken as the detection quantity, and the inverse ratio of the correlation time and the real-time code time difference is taken as the weighting coefficient. The probability distribution characteristics of the detected quantity were further analyzed, and the optimal detection threshold was determined based on the Neyman-Pearson theory. By comparing the detected quantity and the detection threshold, the existence of deception interference was determined. Based on the Scenario 4 set published by the University of Texas, the test results show that compared with typical SQM algorithms such as Ratio and ELP, the proposed algorithm has both high detection probability and fast early warning response time under different false alarm rates.

     

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