SpoofTrackBench：面向欺骗感知无人机跟踪与基准测试的可解释人工智能 (SpoofTrackBench: Interpretable AI for Spoof-Aware UAV Tracking and Benchmarking)

SpoofTrackBench is a reproducible, modular benchmark for evaluating adversarial robustness in real-time localization and tracking (RTLS) systems under radar spoofing. Leveraging the Hampton University Skyler Radar Sensor dataset, we simulate drift, ghost, and mirror-type spoofing attacks and evaluate tracker performance using both Joint Probabilistic Data Association (JPDA) and Global Nearest Neighbor (GNN) architectures. Our framework separates clean and spoofed detection streams, visualizes spoof-induced trajectory divergence, and quantifies assignment errors via direct drift-from-truth metrics. Clustering overlays, injection-aware timelines, and scenario-adaptive visualizations enable interpretability across spoof types and configurations. Evaluation figures and logs are auto-exported for reproducible comparison. SpoofTrackBench sets a new standard for open, ethical benchmarking of spoof-aware tracking pipelines, enabling rigorous cross-architecture analysis and community validation.

翻译：SpoofTrackBench 是一个可复现、模块化的基准测试框架，用于评估雷达欺骗条件下实时定位与跟踪（RTLS）系统的对抗鲁棒性。基于汉普顿大学 Skyler 雷达传感器数据集，我们模拟了漂移型、幽灵型和镜像型欺骗攻击，并采用联合概率数据关联（JPDA）与全局最近邻（GNN）架构对跟踪器性能进行评估。该框架分离了干净与受欺骗的检测数据流，可视化欺骗引发的轨迹偏离，并通过直接真实偏差度量量化分配误差。聚类叠加图、注入感知时间线及场景自适应可视化技术实现了对不同欺骗类型与配置的可解释性分析。评估图表与日志可自动导出以支持可复现的比较。SpoofTrackBench 为开放、伦理化的欺骗感知跟踪流程基准测试设立了新标准，为严格的跨架构分析与社区验证提供了支撑。