Designing Operational Effectiveness of Autonomously Decided Countermeasures

Abstract

It is indispensable that aircrafts in electrical warfare settings endeavour to improve their survivability by selecting optimal countermeasures against threats. In this paper, we model the successful probabilities of aircraft survivability equipments that remove threats encountered, and also propose a framework for the aircrafts to autonomously decide their countermeasures. And then, we design the operational effectiveness of the aircraft survivability equipments, and quantitatively formulate the operational effectiveness into the form of reduction in lethality (RL). We actually show how the operational effectiveness can be computed in simulated example scenarios. To verify our framework proposed in this paper, we experimented with the successful probabilities of aircraft survivability equipments and the autonomous decision-making against threats in various electronic warfare settings. In the experiments, it turns out that our agents outperform the agents that randomly choose their countermeasures, which is 12% more efficient in their performance.

Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques

“…전자전과 관련된 선행연구로는 수신 측정된 변 수들을 활용하여 관련없는 신호를 분리하여 오분석의 확률을 줄이는 연구가 진행되었다 [2] . 또한 항공기가 자신을 위협하는 위협에 대하여 확인하고 최적의 대 응방안을 선정하기 위한 방법론을 제시한 연구 [3] [5][6][7] 분야 및 영상처리/분 류 [8][9][10] 같다.…”