Abstract
Spoofing attacks pose a critical threat to Unmanned Aerial Vehicle (UAV) tracking, inducing trajectory drift, false-target injection, and corrupted multi-sensor measurements that destabilize classical association pipelines such as JPDA and GNN. This paper introduces the Quantum Unified Trajectory Estimator (CUTE-SpoofTrack), a quantum-inspired framework and benchmark for resilient multi-sensor UAV tracking under adversarial spoofing. The system integrates SpoofNet, a deep spoof-detection module that identifies temporal and spatial spoof signatures, with confidence-aware fusion and a Deep Quantum Neural Network (DQNN) that performs assignment reasoning through qubit superposition, Hadamard branching, and CNOT-based dependency encoding. This architecture maintains multiple association hypotheses and suppresses spoof-dominated states, providing robustness beyond classical argmax-based trackers. CUTE-SpoofTrack includes a reproducible benchmark with diverse spoof types—drift, ghost, mirror, burst, and multi-ghost—and provides interpretability overlays for analyzing assignment dynamics and quantum gate activations. Theoretical analysis establishes continuity, margin-based robustness, KL-stability, and spoof-aware amplitude suppression for the DQNN assignment distribution. Numerical evaluations demonstrate that CUTE significantly improves spoof detection accuracy, fusion stability, assignment consistency, and trajectory continuity compared to CNN,LSTM,GNN,JPDA,andfusion-agnostic baselines, while remaining within real-time operational constraints. The results position CUTE-SpoofTrack as a principled and practical foundation for resilient UAV tracking in adversarial aerial environments.