基于深度学习的战场态势变化速度预测模型

doi:10.16182/j.issn1004731x.joss.201803003

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (3): 785-792.doi: 10.16182/j.issn1004731x.joss.201803003

• 专栏：态势智能认知仿真 • 上一篇下一篇

基于深度学习的战场态势变化速度预测模型

陶九阳^1,2, 吴琳¹, 王驰¹, 褚君达¹, 廖鹰¹, 朱丰¹

1.国防大学联合作战学院,北京 100091;
2.陆军工程大学,江苏南京 210007

收稿日期:2018-01-07 出版日期:2018-03-08 发布日期:2019-01-02
作者简介:陶九阳(1983-),男,山东五莲,博士生,研究方向为运筹分析与军事智能决策。
基金资助:
国家自然科学基金(61403400, 61403401, 61773399, 61703412),军民共用重大研究计划联合基金(U1435218)

A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning

Tao Jiuyang^1,2, Wu Lin¹, Wang Chi¹, Chu Junda¹, Liao Ying¹, Zhu Feng¹

1.Joint Operations College, National Defense University, Beijing 100091, China;
2.Army Engineering University of PLA, Nanjing 210007, China

Received:2018-01-07 Online:2018-03-08 Published:2019-01-02

摘要/Abstract

摘要： 战场态势变化剧烈程度的度量和估计,对指挥员均衡认知负载,降低决策风险具有重要意义。首先,基于态势要素状态变化过程中产生的自信息量,建立了态势变化速度计算模型;随后,以二维网格中态势要素机动为基本案例,探索了基于深度学习的态势要素趋势预测方法,证明了深度学习模型的损失函数交叉熵等价于态势变化速度;最后,通过实验分析了随着态势要素客观不确定性的增加,态势变化速度和趋势预测准确性的变化情况,得到推论：学习模型对态势要素的趋势预测准确率存在上限,上限值与态势变化速度成反比。

关键词: 不确定性, 信息论, 态势认知, 深度学习, 香农熵

Abstract: To measure and estimate the uncertainty of the battlefield situation is of great significance for the commanders to plan the reconnaissance mission and reduce the risk of decision-making. Based on Shannon's information theory, firstly, methods and a model on measurement of situation change rate are proposed. Secondly, a scene with two-dimensional grid elements maneuvering is established, based on deep learning, the prediction method for maneuvering trend is explored. It is proved that cross entropy is equivalent to situation change rate. Finally, with the increase of the objective uncertainty, situation change rate and the accuracy of the forecast is analyzed. It is deduced that there is an upper limit on the prediction accuracy based on the learning model, and the upper limit is inversely proportional to the situation change rate.

Key words: information theory, situation awareness, deep learning, shannon entropy

中图分类号:

TP391.9

陶九阳, 吴琳, 王驰, 褚君达, 廖鹰, 朱丰. 基于深度学习的战场态势变化速度预测模型[J]. 系统仿真学报, 2018, 30(3): 785-792.

Tao Jiuyang, Wu Lin, Wang Chi, Chu Junda, Liao Ying, Zhu Feng. A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning[J]. Journal of System Simulation, 2018, 30(3): 785-792.

参考文献 22

[1]	Endsley M R.Measurement of Situation Awareness in Dynamic Systems[J]. Human Factors (S0018-7208), 1995, 37(1): 65-84.
[2]	Endsley M R.Toward a Theory of Situation Awareness in Dynamic Systems[J]. Human Factors (S0018-7208), 1995, 37(1): 32-64.
[3]	Endsley M R.Situation awareness misconceptions and misunderstandings[J]. Journal of Cognitive Engineering & Decision Making (S1555-3434), 2015, 9(1): 4-32.
[4]	Endsley B M R, Garland D J. Situation Awareness: Analysis and Measurement[J]. Crc Press, 2000, 6(2): 1740-1741.
[5]	Strater L, Bolstad C.Simulation-based situation awareness training[M]//Human Factors in Simulation and Training, 2008.
[6]	Muehlethaler C M, Knecht C P.Situation Awareness Training for General Aviation Pilots using Eye Tracking[J]. Ifac Papersonline (S2405-8963), 2016, 49(19): 66-71.
[7]	Endsley M R.Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition[J]. Ergonomics (S0018-7208), 2013, 56(4): 727.
[8]	Salmon P, Stanton N, Walker G, et al.Situation awareness measurement: a review of applicability for C4I environments[J]. Applied Ergonomics (S0003-6870), 2006, 37(2): 225-238.
[9]	Endsley M R.Towards a New Paradigm for Automation: Designing for Situation Awareness[J]. IFAC Proceedings Volumes (S1474-6670), 1995, 28(15): 365-370.
[10]	Parasuraman, Raja, Sheridan, et al. Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs[J]. Journal of Cognitive Engineering & Decision Making (S1555-3434), 2008, 2(2): 140-160.
[11]	Holsopple J, Sudit M, Nusinov M, et al.Enhancing situation awareness via automated situation assessment[J]. IEEE Communications Magazine (S0163-6804), 2010, 48(3): 146-152.
[12]	David S Alberts.The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors[M]. CCRP, 2013.
[13]	陶九阳, 吴琳, 胡晓峰. AlphaGo技术原理分析及人工智能军事应用展望[J]. 指挥与控制学报, 2016(2): 114-120.Tao Jiuyang, Wu Lin, Hu Xiaofeng. Principle Analysis on AlphaGo and Perspective in Milltary Application of Artificial Intelligence[J]. JOURNAL OF COMMAND AND CONTROL, 2016(2): 114-120.
[14]	Dekker S W A, Hummerdal D H, Smith K. Situation awareness: Some remaining questions[J]. Theoretical Issues in Ergonomics Science (S1463-922X), 2010, 11(1/2): 131-135.
[15]	John Patrick, Philip L M.Approaches to understanding, analysing and developing situation awareness[J]. Theoretical Issues in Ergonomics Science (S1463-922X), 2010, 11(1): 41-57.
[16]	Sirkin D, Martelaro N, Johns M, et al.Toward Measurement of Situation Awareness in Autonomous Vehicles[C]//CHI Conference on Human Factors in Computing Systems. ACM, 2017: 405-415.
[17]	Loia V, D’Aniello G, Gaeta A, et al. Enforcing situation awareness with granular computing: a systematic overview and new perspectives[J]. Granular Computing (S2364-4966), 2016, 1(2): 127-143.
[18]	Silver D, Huang A, Maddison C J, et al.Mastering the game of Go with deep neural networks and tree search[J]. Nature (S0028-0836), 2016, 529(7587): 484-489.
[19]	Endsley M R, Kaber D B.Level of automation effects on performance, situation awareness and workload in a dynamic control task[J]. Ergonomics (S0018-7208), 1999, 42(3): 462-492.
[20]	Endsley M R.The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task[J]. Theoretical Issues in Ergonomics Science (S1463-922X), 2004, 5(2): 113-153.
[21]	朱雪龙. 应用信息论基础[M]. 北京:清华大学出版社有限公司, 2001. Zhu Xuelong. Fundamentals of Applied Information Theory [M]. Beijing: Tsinghua University Press, 2001.
[22]	Javier Pinto, Cristina Sernadas, Paulo Mateus.Non- determinism and uncertainty in the situation calculus[J]. International Journal of Uncertainty, Fuzziness and Knowledge- Based Systems (S0218-4885), 2000, 8(2): 127-149.

基于深度学习的战场态势变化速度预测模型

A Model for Battlefield Situation Change Rate Prediction Based on Deep Learning

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	康旭, 张晓峰. 基于生成对抗神经网络的雷达遥感数据增广方法[J]. 系统仿真学报, 2022, 34(4): 920-927.
[2]	聂凯, 曾科军, 孟庆海. 基于深度贝叶斯网络学习的不确定性建模方法[J]. 系统仿真学报, 2022, 34(1): 79-85.
[3]	贺筱媛, 郭圣明, 吴琳, 李东, 许霄, 李丽. 面向智能化兵棋的认知行为建模方法研究[J]. 系统仿真学报, 2021, 33(9): 2037-2047.
[4]	林硕, 安磊, 高治军, 单丹, 尚文利. 结合栈式自编码及长短时记忆的入侵检测研究[J]. 系统仿真学报, 2021, 33(6): 1288-1296.
[5]	程文聪, 史小康, 王志刚. 基于生成对抗网络的仿真卫星云图生成方法[J]. 系统仿真学报, 2021, 33(6): 1297-1306.
[6]	董书琴, 张斌. 面向不平衡数据的网络流量异常检测方法[J]. 系统仿真学报, 2021, 33(3): 679-689.
[7]	胡越, 许凯, 秦龙, 尹全军, 查亚兵. 时空记忆模型在空间认知行为建模中的应用分析[J]. 系统仿真学报, 2021, 33(2): 249-261.
[8]	王霄汉, 张霖, 任磊, 谢堃钰, 王昆玉, 叶飞, 陈真. 基于强化学习的车间调度问题研究简述[J]. 系统仿真学报, 2021, 33(12): 2782-2791.
[9]	王步维, 王敏, 范谦, 王雅男, 章涵文, 乐云亮. 基于深度学习的晶体性质预测研究[J]. 系统仿真学报, 2021, 33(12): 2854-2863.
[10]	冯晓, 张辉, 周蕊, 乔璐, 魏东, 李丹丹, 张玉尧, 郑国清. 基于深度学习和籽粒双面特征的玉米品种识别[J]. 系统仿真学报, 2021, 33(12): 2983-2991.
[11]	孟杨凯, 王正, 范加利. 基于禁忌算法对不确定性舰载机保障的调度优化研究[J]. 系统仿真学报, 2021, 33(10): 2363-2371.
[12]	杜金莲, 李淑飞, 金雪云. 三维烟雾流场超分辨率数据生成网络模型的研究[J]. 系统仿真学报, 2021, 33(10): 2381-2389.
[13]	刘瑞军, 王向上, 张晨, 章博华. 基于深度学习的视觉SLAM综述[J]. 系统仿真学报, 2020, 32(7): 1244-1256.
[14]	阴敬方, 朱登明, 石敏, 王兆其. 基于引导对抗网络的人体深度图像修补方法[J]. 系统仿真学报, 2020, 32(7): 1312-1321.
[15]	戢晓峰, 戈艺澄. 基于深度学习的节假日高速公路交通流预测方法[J]. 系统仿真学报, 2020, 32(6): 1164-1171.