多智能体航天器系统的信息驱动故障检测与识别：协同在轨巡检任务
Information-Driven Fault Detection and Identification for Multi-Agent Spacecraft Systems: Collaborative On-Orbit Inspection Mission
AI总结：
针对低轨多航天器协同巡检任务，提出全局到局部、任务感知的FDI框架，通过统一代价泛函连接制导控制与FDI，实现故障检测识别，仿真验证其在不确定性下的可靠性，为弹性自主巡检提供基础。
摘要：
本文针对低地球轨道执行协同巡检任务的多航天器系统，提出一种全局到局部、任务感知的故障检测与识别（FDI）框架。巡检任务由全局信息驱动的代价泛函表示，该泛函整合了传感器模型、航天器位姿和任务级信息增益目标。此公式通过使用同一代价函数驱动全局任务分配与局部感知或运动决策，将制导、控制与FDI相连接。故障检测通过预期与观测任务指标的比较实现，而高阶代价梯度测量可识别传感器、执行器和状态估计器中的故障。自适应阈值机制捕捉时变巡检几何与动态任务条件。典型多航天器巡检场景的仿真结果表明，该框架在不确定性下的故障定位与分类具有可靠性，为弹性自主巡检架构提供了统一的信息驱动基础。
英文摘要：
This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low Earth orbit. The inspection task is represented by a global information-driven cost functional that integrates the sensor model, spacecraft poses, and mission-level information-gain objectives. This formulation links guidance, control, and FDI by using the same cost function to drive both global task allocation and local sensing or motion decisions. Fault detection is achieved through comparisons between expected and observed task metrics, while higher-order cost-gradient measures enable the identification of faults among sensors, actuators, and state estimators. An adaptive thresholding mechanism captures the time-varying inspection geometry and dynamic mission conditions. Simulation results for representative multi-spacecraft inspection scenarios demonstrate the reliability of fault localization and classification under uncertainty, providing a unified, information-driven foundation for resilient autonomous inspection architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
