基于频谱分解和注意力机制的雷达回波外推算法  

Radar Echo Extrapolation Algorithm Based on Spectrum Decomposition and Spatiotemporal Attention Mechanism

作　　者：金锐 方璘王昊 JIN Rui;FANG Linwanghao(Institute of Air Traffic Management,Civil Aviation Flight University of China,Guanghan 618307,Sichuan,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618307

出　　处：《科技和产业》2023年第8期259-267,共9页Science Technology and Industry

摘　　要：针对雷达回波数据,首次提出使用频谱分解的方式预先提取多尺度回波信息作为深度学习网络的输入数据,以丰富输入数据的特征信息。模型主框架采用UNet神经网络,并且针对模型退化问题,加入了残差连接结构。基于以上设计,提出SpAt-ResUNet预测模型。试验结果表明,相比于传统雷达回波外推算法SPROG与深度学习网络ResUNet模型,该模型对于未来1 h的雷达回波预测长时间外推图像模糊的问题以及强回波留存能力分别得到改善和增强。For the radar echo data,the method of spectral decomposition is proposed for the first time to extract multi-scale echo information in advance as the input data of the depth learning network to enrich the feature information of the input data.The main frame of the model adopts UNet neural network,and the residual connection structure is added to solve the problem of model degradation.Based on the above design,SpAt ResUNet prediction model is proposed.The test results show that compared with the traditional radar echo extrapolation method SPROG and the depth learning network RESUNet model,the model has improved and enhanced the problem of long-term extrapolation image blurring and strong echo retention ability for the radar echo prediction in the next hour.

关 键 词：频谱分解 注意力机制 深度学习网络 雷达回波外推 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]