基于高斯过程回归的多快拍RFI定位融合算法  

Multi snapshot RFI localization fusion algorithm based on gaussian process regression

作　　者：赵洋[1] 靳榕[1] 李一楠[2] 窦昊锋 ZHAO Yang;JIN Rong;LI Yinan;DOU Haofeng(Research Center of 6G Mobile Communications,School of Cyber Science and Engineering and School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China;China Academy of Space Technology(Xi’an),Xi’an 710000,China)

机构地区：[1]华中科技大学网络空间安全学院第六代移动通信研究中心,武汉430074 [2]中国空间技术研究院西安分院,西安710000

出　　处：《空间电子技术》2025年第2期75-85,共11页Space Electronic Technology

基　　金：民用航天十四五预先研究项目(编号:D040202)。

摘　　要：综合孔径辐射计的接收数据易受射频干扰污染,影响后续产品质量。射频干扰源的精确定位是处理射频干扰的关键步骤。在以往的研究中,通过对多快拍中同一射频干扰源的定位结果做平均以提高其定位精度。然而,同一射频干扰源在不同快拍中的位置和强度不同,导致不同快拍中同一射频干扰源的定位精度不同。因此,简单平均算法难以获得最优的定位精度。首先,文章提出了一种基于高斯过程回归的多快拍射频干扰定位融合算法。该算法通过高斯过程回归对射频干扰源不同方向的定位误差数据进行回归学习得到相应的定位误差估计模型;其次,使用该模型对每张快拍中射频干扰源在不同方向上的定位误差进行估计,并以此为标准给各快拍中射频干扰不同方向上的定位结果分配权重;最后,通过加权融合得到射频干扰源的精确定位。通过仿真实验,验证了该方法相比于简单平均算法的优越性。此外,文章使用土壤湿度和海洋盐度卫星数据进行了实验验证,证明了该方法的合理性和实用性。The received data of synthetic aperture interferometric radiometer is susceptible to radio frequency interference(RFI) contamination,which affects the quality of subsequent products.The accurate localization of RFIs is a key to eliminating or mitigating the effects of RFI.In previous studies,the localization accuracy was improved by averaging the localization results of the same RFI in multiple snapshots.However,the localization and intensity of the same RFI vary in different snapshots,resulting in different localization accuracies of the same RFI in different snapshots.Therefore,simple averaging algorithms are difficult to achieve optimal localization accuracy.This article proposes a multi snapshot RFI localization fusion algorithm based on gaussian process regression(GPR).The algorithm obtains the corresponding localization error estimation model by using the GPR model to perform regression learning on the localization error data of the RFI in different directions.Then,the model is used to estimate the localization error of RFIs in different directions for each snapshot,and weights are assigned to the localization results of RFI in different directions for each snapshot based on this standard.Finally,the precise localization of the RFI was obtained through weighted fusion,which improved the localization accuracy of RFI.Through simulation experiments,the superiority of this method over the simple averaging algorithm was verified.In addition,this paper conducted experimental verification using soil moisture and ocean salinity satellite data,demonstrating the rationality and practicality of this method.

关 键 词：射频干扰定位 高斯过程回归 数据融合 

分 类 号：V443[航空宇航科学与技术—飞行器设计] TN253[电子电信—物理电子学]