A Data-Driven Model for Evaluating the Survivability of Unmanned Aerial Vehicle Routes

Evaluating unmanned aerial vehicle (UAV) survivability is crucial when UAVs are required to perform missions in hostile areas. There are complex spatiotemporal interactions among entities in hostile areas; therefore, evaluation of the survivability of a UAV flying along a specific route needs to effectively fuse spatiotemporal information. It is difficult to clarify how information is fused and how threats accumulate along the route. We present a novel solution for building a learnable evaluation model that can extract the required knowledge directly from the data. In this approach, hostile scenarios are decomposed into various threat entities, threat relations (TRs) and UAVs, where a TR is the relation between a threat entity and a UAV. We propose a data-driven evaluation model named the sequential threat inference network (STIN), which can learn TRs and perform spatiotemporal fusion to evaluate survivability. We validate the model in multiple scenarios that contain threat entities of different types, quantities and attributes. The results show that the STIN is superior to the baseline models in various situations. Specifically, the STIN can automatically generalize learned knowledge to scenarios with different numbers of threat entities without retraining. In the generalization experiment, the error increases little when the STIN is directly used in the new scenarios where the number of entities is larger than in the training scenarios.