• 한국국방기술학회 논문지
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• 제5권1호
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• pp.19-24
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• 2023

신기술의 발전으로 인해 미래 전장의 모습은 지금과는 크게 달라질 것으로 예상된다. 특히 AI와 로봇이 전장의 주역으로 참여하고 이들의 성능이 고도화될 경우 전쟁 결과는 예측하기 어려워질 것이다. 이에 미국, 중국을 비롯한 주요 국가들은 인공지능(AI)을 국가 경쟁력과 미래전의 양상을 바꿀 수 있는 핵심기술 및 게임체인저로 판단하고 관련기술 선점과 AI무기체계 개발에 국가 차원의 총력을 기울이고 있으며, 따라서 세계 각국은 국방AI를 추진하기 위한 전략 수립과 정책 마련은 물론 관련 조직 신설 등 인프라 확충에도 적극적으로 나서고 있다. 우리 군도 육군이 조직과 업무수행체계 면에서 가장 선도적으로 국방AI를 추진하고 있으나, 이를 국방 차원에서 통합적으로 관리하고 조정통제하는 거버넌스 부재로 어려움과 혼란을 겪고 있는 것이 현실이다. 최근 국방AI센터(가칭) 창설의 필요성에 대한 공감대가 형성되고 있는 가운데 연구자는 미국, 영국, 호주 등 해외 주요국의 국방AI 추진현황을 분석하고 국방AI 정책 수립에 관한 몇 가지 시사점을 제시하고자 한다.

• 정보보호학회논문지
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• 제33권3호
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• pp.577-589
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• 2023

국방부는 국방혁신 4.0 계획에 기반한 첨단과학기술군 육성을 위해 AI를 향후 전력 증강의 핵심 기술로 활용할 계획이다. 그러나 AI의 특성에 따른 보안 위협은 AI 기반의 국방정보시스템에 실질적인 위협이 될 수 있다. 이를 해소하기 위해서는 최초 개발 단계에서부터 체계적인 보안 활동의 수행을 통한 보안 내재화가 필요하다. 이에 본 논문에서는 AI 기반 국방정보시스템 개발 시 단계별로 수행해야 하는 보안 활동 수행 방안을 제안한다. 이를 통해 향후 국방 분야에 AI 기술 적용에 따른 보안 위협을 예방하고 국방정보시스템의 안전성과 신뢰성을 확보하는데 기여할 수 있을 것으로 기대한다.

• 한국군사과학기술학회지
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• 제27권5호
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• pp.619-627
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• 2024

As AI technology advances, interest in performing multi-robot autonomous missions for manned-unmanned teaming (MUM-T) is increasing. In order to develop autonomous mission performance technology for multiple robots, simulation technology that reflects the characteristics of real robots and can flexibly apply various missions is needed. Additionally, in order to solve complex non-linear tasks, an API must be provided to apply multi-robot reinforcement learning technology, which is currently under active research. In this study, we propose the campaign model to flexibly simulate the missions of multiple robots. We then discuss the results of developing a simulation environment that can be edited and run and provides a reinforcement learning API including acceleration performance. The proposed simulated control module and simulated environment were verified using an enemy infiltration scenario, and parallel processing performance for efficient reinforcement learning was confirmed through experiments.

• 산업경영시스템학회지
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• 제46권4호
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• pp.229-237
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• 2023

In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.

• 전자통신동향분석
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• 제39권4호
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• pp.21-31
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• 2024

Major global powers are investing heavily in artificial intelligence (AI) and hyper-connected networks, demonstrating their crucial role in future warfare. To advance and utilize AI in national defense, it is essential to have policy support at the governmental or national level. This includes establishing a research and development infrastructure, creating a common development environment, and fostering AI expertise through education and training programs. To achieve advancements in hyper-connected networks, it is essential to establish a foundation for a robust and resilient infrastructure by comprehensively building integrated satellite, aerial, and ground networks, along with developing 5G & edge computing and low-orbit satellite communication technologies. This multi-faceted approach will ensure the successful integration of AI and hyper-connected networks, strengthening national defense and positioning nations at the forefront of technological advancements in warfare.

• 한국군사과학기술학회지
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• 제25권6호
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• 한국군사과학기술학회지
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• 제25권3호
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• 한국군사과학기술학회지
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• 제26권2호
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• pp.188-196
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• 2023

In the future battlefield centered on the concept of mosaic warfare, the need for an unmanned combat system will increase to value human life. It is necessary for Multiple/Heterogeneous Unmanned Combat Systems to have suitable mission planning method in order to perform various mission. In this paper, we propose the MTSR model for mission planning of the unmanned combat system, and introduce a method of identifying a task by a combination of services using a request operator and a method of allocating resources to perform a task using the requested service. In order to verify the performance of the proposed task-resource matchmaking algorithm, simulation using occupation scenarios is performed and the results are analyzed.

• 문화기술의 융합
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• 제10권3호
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• pp.19-24
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• 2024

미래의 군사 전투 환경은 현재의 군(軍) 인구 감소 및 변화하는 양상에 맞춰 국방 분야에서 인공지능(AI)의 역할과 중요성이 급격히 확대되고 있다. 특히, 민간에서의 AI(Artificial Intelligence) 개발은 OpenAI의 Chat-GPT 등장 이후 초거대 AI(Super-Giant AI, also known as Hyperscale AI), 즉 파운데이션 모델을 기반으로 새로운 영역에서 부상하고 있다. 미국 국방부는 CDAO(Chief Digital and AI Office) 산하의 Task Force Lima를 조직하여 LLM(Large Language Model)과 생성형 AI의 활용 방안에 대한 연구를 진행 중이며, 중국, 이스라엘 등 군사 선진국에서도 초거대 AI를 군에 적용하기 위한 연구를 수행 중이다. 따라서, 우리 군도 무기체계에 초거대 AI 모델의 활용 가능성과 적용분야에 대한 연구의 필요성이 대두되고 있다. 본 논문에서는 기존의 특화 AI와 초거대 AI(파운데이션 모델, Foundation Model)의 특징 및 장·단점을 비교하고, 무기체계에 적용될 수 있는 초거대 AI의 새로운 적용분야를 발굴하였다. 본 연구는 미래의 적용 분야와 잠재적인 도전과제에 대한 예측과 함께 초거대 인공지능을 국방작전에 효과적으로 통합하기 위한 통찰력을 제공하고, 선진화된 인공지능 시대에서의 국방 정책 개발, 국제 안보 전략을 형성하는 데 기여할 것으로 기대한다.

• 한국군사과학기술학회지
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• 제25권1호
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.