摘要
针对水下平台水下对抗作战量化验证评估困难、指导机动规避作战模型欠缺等问题,设计了一种基于大数据学习的水下对抗预测模型。首先进行水下平台水下对抗建模,基于蒙特卡洛方法执行若干轮次仿真获得规避概率数据集;同时,为解决海量仿真下时间效率不佳的问题,提出利用BP神经网络预测算法进行数据学习,提供准确、快速、可视化的对抗结果。试验结果表明,在本文设定的试验环境下,基于BP神经网络预测算法的平均预测误差为7.28%,可有效对水下平台规避概率进行预测,为指挥员指挥决策提供数据支撑。
Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model,a prediction model of underwater countermeasures based on big data learning is designed.Firstly,the underwater countermeasure modeling of the underwater platform is carried out,and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method.At the same time,in order to solve the problem of poor time efficiency under massive simulation,BP neural network prediction algorithm is proposed for big data learning to provide accurate,fast and visual antagonistic results.The test results show that under the test environment set in this paper,the average prediction error of BP neural network prediction algorithm is 728%respectively,which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander's command decision.
作者
罗立峰
张静远
华明
封皓君
于莹
LUO Lifeng;ZHANG Jingyuan;HUA Ming;FENG Haojun;YU Ying(Naval University of Engineering,Wuhan 430033,China;Unit 92858 of PLA,Ningbo 315812,China)
出处
《指挥控制与仿真》
2024年第2期29-34,共6页
Command Control & Simulation
关键词
软对抗
大数据
对抗预测模型
规避概率
soft confrontation
big data
adversarial prediction model
avoidance probability
