Real-Time UAV Sensor Fault Diagnosis Under Mission-Level Navigation Performance Constraints With MR-IGATNet

Reliable real-time fault diagnosis of onboard sensors is essential for UAV navigation and localization in safety-critical missions. Although recent fault detection and diagnosis (FDD) methods have improved robustness to class imbalance and limited fault data, incipient and non-catastrophic faults under asynchronous multi-rate sensing and onboard resource constraints remain insufficiently addressed. In this work, we formulate a real-time UAV sensor fault diagnosis problem under mission-level navigation performance constraints, where the operational regime is defined by mission-level navigation error criteria rather than catastrophic mission failure. To address this problem, we propose MR-IGATNet, a multi-rate multi-stream diagnosis network that combines InceptionTime window encoding with graph attention based fusion for asynchronous multi-sensor data. MR-IGATNet adopts a group-wise architecture aligned with feature semantics and sensor update frequencies. It further introduces a dual-level fusion module conditioned on asynchronous update intervals (Δt), combining graph-attention message passing with query-based pooling (GAT-QP) to integrate information across feature groups and sensor streams. Extensive offline and hardware-in-the-loop (HIL) evaluations show consistent improvements over strong baselines, while real-time deployment results confirm 9.98 ms latency suitable for onboard diagnosis.