摘要
随着雷达使用频段提升,降雨、云雾等气象因素对雷达探测的影响逐渐不可忽视。为研究气象粒子群对雷达探测工作产生的影响,文章总结了电磁波在不同类型气象粒子群下的谱分布与衰减特性;再根据粒子谱方程给出气象粒子群的模型信息;最后基于矢量辐射传输方法,提出了一种改进的气象粒子群时域回波快速仿真的方法,包括分区加速、传播路径判断的优化,在大体量粒子群回波建模中的优化效果可达到普通方法的20倍左右。结果表明,雨粒子群对雷达探测工作会造成较大的杂波,其杂波幅值约为发射功率的10-4倍;云粒子群的时域结果存在较强的随机性;雾粒子群时域结果均匀且幅值较低。文章针对不同类型气象粒子群时域回波的仿真结果与分析,对雷达的精准探测研究具有一定参考价值。
With the increase of radar frequency band,the influence of rain,cloud,fog and other meteorological factors on radar detection gradually cannot be ignored.In order to analyze the influence of meteorological particle clusters on radar detection,this paper summarizes the spectral distribution and attenuation characteristics of electromagnetic waves under different types of meteorological particle clusters.Then,the model information of meteorological particle clusters is given according to the particle spectrum equation.Finally,based on the vector radiative transfer method,an improved method for fast simulation of particle clusters time-domain echoes is proposed,including the optimization of partition acceleration and propagation path judgment.The optimization effect in large-volume clusters modeling can reach about 20 times of the ordinary method.The results show that the rain clusters cause large clutter to the radar detection,and its clutter amplitude is about 10-4 times of the transmit power.The time-domain results of cloud clusters have strong randomness,and the time-domain results of fog clusters are uniform and of low amplitude.This paper presents the simulation results and analysis of the time-domain echoes of different types of meteorological particle clusters,which have certain value of reference for the precise detection research of radar.
作者
李丹阳
左炎春
吴迪龙
罗熹
刘伟
王蕊
LI Danyang;ZUO Yanchun;WU Dilong;LUO Xi;LIU Wei;WANG Rui(School of Physics,Xidian University,Xi’an 710000,China;China Academy of Space Technology(Xi’an),Xi’an 710000,China)
出处
《空间电子技术》
2024年第2期34-41,共8页
Space Electronic Technology
基金
国家重点实验室基金项目(编号:HTKJ2021KL504004)。
关键词
雷达时域回波
矢量辐射传输方法
气象粒子
分区算法
radar echoes
vector radiative transfer method
meteorological particles
partition algorithm