[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 深度学习研究及军事应用综述-Review of Deep Learning Research and Application of New Scientific Discoveries to Military
文章摘要
王晓丹, 向前, 李睿, 来杰.深度学习研究及军事应用综述[J].空军工程大学学报:自然科学版,2022,23(1):1-11
深度学习研究及军事应用综述
Review of Deep Learning Research and Application of New Scientific Discoveries to Military
  
DOI:
中文关键词: 深度学习  卷积神经网络  循环神经网络  自编码器  生成对抗网络  目标识别  态势感知  指挥决策
英文关键词: deep learning  convolution neural networks  recurrent neural networks  auto encoders  generative adversarial networks  target recognition  situational awareness  command decision
基金项目:国家自然科学基金(61876189)
作者单位
王晓丹, 向前, 李睿, 来杰 1.空军工程大学防空反导学院 西安 710051 2. 61932部队 北京 100089 
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中文摘要:
      深度学习作为当前人工智能领域的研究热点之一,已经受到广泛关注。借助于强大的特征表示和学习能力,深度学习日益成为军事领域智能化发展的技术基础。首先结合深度学习的最新发展,指出深度学习的快速发展得益于理论的突破、计算机运算能力的显著提高和开源软件的广泛流行,着重梳理了目前主要的深度学习硬件平台和编程框架,并总结了各自的特点和研究进展;然后对深度学习在目标识别、态势感知、指挥决策等典型军事领域的应用和存在的不足进行了总结;最后,分析了深度学习军事应用面临的挑战,包括数据获取困难、处理不确定不完备信息和多域信息能力不足、精确度和实时性较低、可解释和可理解性不强等,并针对这些问题展望了未来可能的发展方向和趋势。
英文摘要:
      As one of the current research hotspots in artificial intelligence, deep learning has received widespread attention. With the powerful feature representation and learning capability, the deep learning has increasingly become the technical basis for the development of intelligence in the military field. In combination with the latest development of deep learning, this paper points out that the rapid development of deep learning is attributed to the breakthrough of theory, the remarkable improvement of computer computing power and the widespread popularity of open source software, focuses on combing the current main deep learning hardware platforms and programming frameworks, and summarizes the characteristics and research progress of each. Further, the application and shortcomings of the deep learning in typical military areas such as target recognition, situational awareness and command decision are summarized. Finally, the challenges faced by military application of deep learning are analyzed, including difficulties in data acquisition, insufficient ability to handle uncertain and incomplete information and multi domain information, low accuracy and real time, and poor interpretability and comprehensibility. In view of such challenges mentioned above, the paper looks forward to the future, the possible development directions, and the trend.
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