基于机器学习的海上蒸发波导特性智能化预测  

Intelligent Prediction for Marine Evaporation Duct Characteristics Based on Machine Learning

作　　者：石广亮 杨铖 王健 SHI Guangliang;YANG Cheng;WANG Jian(School of Microelectronics,Tianjin University,Tianjin 300072,China;Qingdao Institute for Ocean Technology,Tianjin University,Qingdao Shandong 266200,China;State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,Luoyang Henan 473003,China)

机构地区：[1]天津大学微电子学院,天津300072 [2]天津大学青岛海洋技术研究院,山东青岛266200 [3]电子信息系统复杂电磁环境效应国家重点实验室,河南洛阳473003

出　　处：《指挥与控制学报》2022年第2期198-205,共8页Journal of Command and Control

基　　金：电子信息系统复杂电磁环境效应国家重点实验室(CEMEE2022G0201)资助。

摘　　要：为了提高海上作战环境中蒸发波导特性预测的准确性,支撑系统规划设计、作战指挥控制以及占领电磁环境和频谱战优势,引入机器学习的方法,重建了一种蒸发波导特性智能化预测模型.对比分析结果显示,所建模型与实测剖面偏差为0.45 M-unit,相对误差降低到0.14%,均方根误差为1.20m,相比于Gerstoft模型精度提升量为3.46%.该方法预期在提高中国区域乃至全球范围内预测蒸发波导高度的准确性方面具有很大的潜力.To improve the accuracy of evaporation duct prediction in the maritime combat environment,machine learning methods are introduced to reconstruct a prediction model of evaporation duct characteristics.It can support radio system design,combat command and control,and seize the advantages of electromagnetic spectrum warfare.The comparative results show that the deviation between the proposed model and the measured profile is 0.45 M-unit,and the relative error is reduced to 0.14%.The root-mean-square error is 1.20 m,which is an increase of 3.46%in accuracy compared to the Gerstoft model.This method is expected to have great potential in predicting the height of the evaporation duct in China and even globally.

关 键 词：作战指挥 蒸发波导 机器学习 智能化预测 

分 类 号：E91[军事] TP181[自动化与计算机技术—控制理论与控制工程]