A Data-Driven Model for Evaluating the Survivability of Unmanned Aerial Vehicle Routes
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A Data-Driven Model for Evaluating the Survivability of Unmanned Aerial Vehicle Routes Jun Guo 1 & Wei Xia 1 & Huawei Ma 1 & Xiaoxuan Hu 1 Received: 18 August 2019 / Accepted: 1 April 2020 # Springer Nature B.V. 2020
Abstract 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. Keywords Data-driven . Relation learning . Threat assessment . Route survivability
1 Introduction Unmanned aerial vehicles (UAVs) are increasingly used to perform missions in hostile areas. When UAVs pass through hostile areas along pre-set flight routes, they may be tracked, attacked or even destroyed by air defence systems. Decisionmaking methods for UAVs usually focus on how choosing a low-risk route, and assessing the survivability of a UAV flying along a route is crucial to identify the safest route for the UAV [1–5]. Using the survivability of the UAVat the route endpoint
* Wei Xia [email protected] Jun Guo [email protected] Huawei Ma [email protected] Xiaoxuan Hu [email protected] 1
School of Management, HeFei University of Technology, HeFei 230009, AnHui, China
when a UAV flies along a route is a common and compact method to represent the survivability of routes [6, 7]. In this paper, the survivability of the UAV flying along a route is referred to as the survivability of the route. Assessing the survivability of a route corresponds to a Level-3 threat assessment in the Joint Directors of Laboratories (JDL) model [6] because it concerns future harm to the UAV. In hostile areas, a UAV is threatened by multiple threats from the opponent’s entities. The risk to the UAV accumulates along the route, and the survivability of the UAV decreas
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