Reliability, Robustness, and Resilience Modeling for Surveillance System in Advanced Air Mobility Operations
For stakeholders in Advanced Air Mobility (AAM), this work provides a comprehensive modeling approach to design surveillance systems that maintain performance under various disruptions, addressing a critical safety and efficiency need.
This study develops a 3R (reliability, robustness, resilience) modeling framework for the Surveillance for Advanced Air Mobility (SAM) system to optimize multi-type sensor network design and operation under normal and off-nominal conditions. The framework determines baseline sensor requirements, additional sensors for perturbations, and backup strategies for failures, aiming to ensure safe AAM operations.
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.