Hard Sample Mining for GNSS Spoofing Detector via Physics-Based Meta-Learning Optimization

GNSS spoofing detectors flag spoofing signals through sensing features such as carrier-to-noise density ratio and signal quality monitoring statistics. Training stronger detectors—and mapping detector performance boundaries—requires hard traction samples that systematically challenge those features, yet such samples are expensive to obtain under closed-loop replay and vary with the traction target. We propose Physics-Based Meta-Learning Optimization (PBMO) as a hard sample miner: it archives traction trajectories across traction targets, warm-starts a new traction target with Offline-RGPE and physics-based priors, and refines candidates with Physics-prior Guided Pattern Search (PhGPS). Hardness is scored by a conformal upper bound on the fraction of moving-variance windows that exceed channel thresholds. On a four-channel moving-variance (MV) detector, comparisons show that PBMO lowers the mean conformal upper bound by 30.7% relative to an unoptimized frequency-difference-lock strategy, and under the same online search budget remains below cold-start, nearest-neighbor, and mean-trajectory initializations. A USRP X310 wired loopback at one traction frequency preserves this ordering.