Blind Code Estimation of GNSS SCER Spoofing Based on Conditional Entropy Measurement

The SCER spoofing attack constitutes an advanced spoofing targeting non-cooperative GNSS receivers, wherein the accuracy of code estimation governs the SCER effectiveness. The employment of non-disclosed signal structure in non-cooperative GNSS terminals constrains prior knowledge pertinent to code estimation accuracy solely to relative signal-to-noise ratio (SNR). Current approaches primarily rely on high-gain antennas to improve SNR and reduce bit error rate (BER), while the estimation accuracy is fundamentally constrained by practical hardware costs. This paper proposes a conditional entropy-based technique for code uncertainty measurement and estimation, eliminating antenna cost augmentation. By quantifying the impact of intra-chip SNR on polarity decision posterior probabilities, a chip-level conditional entropy model is constructed to transform code uncertainty into measurable prior knowledge for correction guidance. Following a descending conditional entropy strategy, chips within an optimal correction ratio range are reconstructed into local PRN codes. Ultimately, the optimal estimation result is identified through a comparative correlation analysis based on the local signals with both positive and negative polarities. Simulations with GPS L1 P(Y) signal and practical experiments with GPS L1 C/A signal demonstrate that the proposed method demonstrated maximum reductions in BER of 10.2\% and 8.8\%, respectively.