用信息增益统一任务与故障检测，提升航天器编队巡检的自主可靠性。
Information-Driven Fault Detection and Identification for Multi-Agent Spacecraft Systems: Collaborative On-Orbit Inspection Mission
- 基于信息增益构建全局任务成本函数，联动导航、控制与故障诊断
- 通过任务指标偏差和梯度分析，实现传感器/执行器/估测器的精准定位
- 自适应阈值应对轨道动态变化，适合复杂太空巡检场景
本文提出一种面向低地球轨道多航天器协同巡检任务的全局到局部、任务感知故障检测与识别（FDI）框架。任务由融合传感器模型、航天器位姿与任务级信息增益目标的全局信息驱动成本函数表示。该公式通过同一成本函数同时驱动全局任务分配与局部感知或运动决策，实现引导、控制与FDI的统一。故障检测基于预期与实际任务指标的对比，高阶成本梯度用于识别传感器、执行器及状态估测器的故障。自适应阈值机制捕捉随时间变化的巡检几何与动态任务条件。针对典型多航天器巡检场景的仿真结果表明，在不确定性下仍能可靠实现故障定位与分类，为弹性自主巡检架构提供了统一的信息驱动基础。
原文摘要 · Abstract (English)
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.
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