空间电子技术2025，Vol.22Issue(2)：75-85,11.DOI:10.3969/j.issn.1674-7135.2025.02.009
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
摘要
Abstract
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Key words
radio frequency interference localization/gaussian process regression/data fusion引用本文复制引用
赵洋,靳榕,李一楠,窦昊锋..基于高斯过程回归的多快拍RFI定位融合算法[J].空间电子技术,2025,22(2):75-85,11.基金项目
民用航天十四五预先研究项目(编号:D040202) （编号:D040202）
