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，用于评估雷达欺骗攻击下实时定位与跟踪系统的对抗鲁棒性。基于汉普顿大学Skyler雷达传感器数据集，我们模拟了漂移型、鬼影型和镜像型欺骗攻击，并采用联合概率数据关联与全局最近邻架构对跟踪器性能进行评估。该框架分离了纯净与受欺骗的检测数据流，通过轨迹偏离可视化呈现欺骗效应，并采用直接真值偏移度量对关联误差进行量化。聚类叠加图、注入感知时间轴和场景自适应可视化技术实现了跨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供可复现比较。SpoofTrackBench为欺骗感知跟踪流程的开放化、伦理化基准测试设立了新标准，为跨架构严格分析与社区验证提供了技术支撑。