GNSS Anti-Spoofing: A Sliding Composite Delta Metric Using Maximum Likelihood Estimation

To ensure the safety of global navigation satellite system (GNSS) users, spoofing detection is generally performed using additional correlators, where a detection metric is built to analyze the distortion of the cross correlation function (CCF). Increasing the probability of detection is usually achieved by increasing the number of additional correlators, and yet such measure can induce a heavy computational burden. To address this limitation, we propose a sliding composite delta (SCD) metric, where the threshold is obtained by maximum likelihood estimation (MLE). Using only eight correlators, the proposed metric exhibits sufficient superiority in probability of detection. The interval between the additional correlators can be regularly adjusted to build a composite detection metric, which can facilitate the detection of spoofing signals with varying delays. In addition, the adopted MLE-based method allows for the determination of an optimal detection threshold. Simulation and experiment results show that, compared with the existing metrics, the proposed metric achieves a higher probability of detection and a shorter time to alarm. Therefore, the proposed metric is more suitable for spoofing detection.

Automated summary

The proposed sliding composite delta metric with a threshold obtained by maximum likelihood estimation shows superior performance in spoofing detection with only eight correlators. The interval between the correlators can be adjusted to facilitate the detection of spoofing signals with varying delays.