摘要
在大规模兵棋仿真推演中,空中任务是指挥员关注的重点。对空中任务的快速、准确和自动识别,是智能决策的前提和基础。深度学习技术的迅速发展,为复杂战场态势特征提取提供了现实可行的解决方法,为研究空中任务识别提供了技术支持。概述了传统任务识别研究方法和基于深度学习的任务识别方法研究进展,分别对卷积神经网络(Convolutional Neural Networks,CNN)、长短时记忆网络(Long Short Term Memory,LSTM)、生成对抗网络(Generate Adversarial Network,GAN)3种深度学习方法在空中任务识别问题中的应用进行了论述,提出了解决思路。
In the large-scale simulation of war game, the air mission is the focus of the commander＇s attention. The rapid, accurate and automatic recognition of air missions is the prerequisite and basis for intelligent decision making. The rapid development of deep learning technology provided a practical and feasible solution for the extraction of complex battlefield posture features, and provided technical support for studying air mission recognition. The research progress of the traditional mission recognition research method and the mission recognition method based on the deep learning was summarized. The three methods of deep learning of Convolution Neural Network （CNN）, Long-short Term Memory Network （LSTM） and Generate Adversarial Network （GAN） air mission recognition problem in the application were discussed, putting forward the solution ideas.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2017年第9期2227-2231,共5页
Journal of System Simulation
基金
国家自然科学基金(U1435218
61403401)
关键词
深度学习
任务识别
空中任务
方法研究
deep learning
mission recognition
air mission
method research
