Uncovering Subtle GNSS Spoofing by Decomposing the Complex Cross Ambiguity Function

Details

• Creators:
  Ahmed, Sahil ; Khanafseh, Samer ; Pervan, Boris
• Corporate Creators:
  Illinois Institute of Technology
• Corporate Contributors:
  United States. Department of Transportation. Federal Aviation Administration ; Center for Assured and Resilient Navigation in Advanced Transportation Systems (CARNATIONS) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Antennas
  • Navigation systems
  • Telecommunications
  • Aviation safety
  • Navigation
  • Signals
  • Global Positioning System
  • GNSS spoofing detection
  • cross ambiguity function
  • particle swarm decomposition
  More +
• Resource Type:
  Journal Article
• Geographical Coverage:
  United States
• Corporate Publisher:
  Illinois Institute of Technology
• Abstract:
  In this paper, we introduce a method to uncover Global Navigation Satellite System (GNSS) spoofing by directly decomposing the Complex Cross Ambiguity Function (CCAF) into its authentic and counterfeit components. We demonstrate the importance of using complex cross ambiguity measurements compared to the magnitude-only approaches utilized in prior work on spoofing detection. We also introduce a CCAF error decorrelation method to mitigate the influence of thermal noise. The detector can identify spoofing in the presence of multipath and when the spoofing signal is power-matched with offsets in code delay and Doppler frequency that are close to the authentic signal.
• Format:
  PDF
• Funding:
  MOA 693KA8-21-T-00027 ; 69A3552348324
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/80064/dot_80064_DS1.pdf
• File Type:
  [PDF - 15.03 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3c577b104037246d4904085f2beb8b0dfd86563252fc176d05d84f3eb5a2d6f0c2f59b76bb36894a28896581391cc648225ef533d2198a1ab64b47a888fea795