在低空空域中保障先进空中交通（Advanced Air Mobility, AAM）的安全高效运行，需依赖一套可靠、鲁棒且具韧性的监视系统，该系统须能在正常及非标工况下持续完成航空器的探测、识别与跟踪任务。为满足此需求，本研究构建了一个涵盖可靠性（reliability）、鲁棒性（robustness）与韧性（resilience）的综合3R建模框架，面向先进空中交通监视系统（Surveillance for Advanced Air Mobility, SAM），重点支撑多类型传感器网络的最优设计与运行。在正常运行条件下，可靠性模型确定满足监视覆盖与探测要求所需的基准传感器类型、数量及布设位置；针对外部扰动（如恶劣天气或AAM交通需求突增），鲁棒性模型识别为维持系统性能所必需的额外传感器配置；对于因主用传感器失效导致的监视中断，韧性模型则制定备用传感器部署与调度策略，以提供临时监视覆盖、最小化运行中断，并保障AAM作业的安全延续。

Ensuring the safe and efficient operation of Advanced Air Mobility (AAM) in low-altitude airspace requires a reliable, robust, and resilient surveillance system capable of continuously detecting, identifying, and tracking aircraft under both normal and off-nominal conditions. To address this need, this study develops a comprehensive 3R modeling framework, reliability, robustness, and resilience, for the Surveillance for Advanced Air Mobility (SAM) system, with a focus on the optimal design and operation of a multi-type sensor network. Under normal operating conditions, the reliability model determines the baseline sensor types, quantities, and locations required to satisfy surveillance coverage and detection requirements. To address external perturbations, such as adverse weather conditions or sudden increases in AAM traffic demand, the robustness model identifies additional sensor requirements needed to maintain system performance. Furthermore, for surveillance outages caused by primary sensor failures, the resiliency model develops backup sensor deployment and dispatch strategies to provide temporary surveillance coverage, minimize operational disruptions, and support the safe continuation of AAM operations.