• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

来自低地球轨道卫星的全球导航卫星系统射频干扰监测:实验室原型

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype.

作者信息

Troglia Gamba Micaela, Polidori Brendan David, Minetto Alex, Dovis Fabio, Banfi Emilio, Dominici Fabrizio

机构信息

LINKS Foundation, 10138 Turin, Italy.

Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy.

出版信息

Sensors (Basel). 2024 Jan 13;24(2):508. doi: 10.3390/s24020508.

DOI:10.3390/s24020508
PMID:38257601
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10819834/
Abstract

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

摘要

射频干扰(RFI)对全球导航卫星系统(GNSS)信号的干扰作用是众所周知的,在过去的四十年里,人们对许多应对措施进行了研究。最近,低地球轨道(LEO)卫星被视为监测GNSS RFI的一个好机会,在过去五年里,许多商业和学术项目不断涌现。在此背景下,本文提出了一种新的星载系统,用于检测、分类和定位地面GNSS RFI信号,特别是用于民用的干扰和欺骗信号。本文介绍了将搭载在纳米卫星上的RFI检测软件模块的实现。描述了整个开发工作,包括目标平台和算法的选择、实现、检测性能评估以及计算负载分析。实现了两种RFI检测器:用于非GNSS类干扰(如线性调频干扰)的卡方拟合优度(GoF)算法,以及用于GNSS类干扰(如欺骗)的快照采集。在存在干扰和欺骗信号的情况下的初步测试结果表明,在灵敏度方面具有良好的检测能力,并突出了优化计算负载的空间,特别是对于基于快照采集的RFI检测器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/d5052885dd8e/sensors-24-00508-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/e49a76ac6ca7/sensors-24-00508-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/de1dc6bd3133/sensors-24-00508-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/689e0a296621/sensors-24-00508-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/6474d693dcae/sensors-24-00508-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/91fb24b1f395/sensors-24-00508-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/3d0b567e0bdb/sensors-24-00508-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/dc1cbdc2b7e5/sensors-24-00508-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/d5052885dd8e/sensors-24-00508-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/e49a76ac6ca7/sensors-24-00508-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/de1dc6bd3133/sensors-24-00508-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/689e0a296621/sensors-24-00508-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/6474d693dcae/sensors-24-00508-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/91fb24b1f395/sensors-24-00508-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/3d0b567e0bdb/sensors-24-00508-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/dc1cbdc2b7e5/sensors-24-00508-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b72/10819834/d5052885dd8e/sensors-24-00508-g011.jpg

相似文献

1
GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype.来自低地球轨道卫星的全球导航卫星系统射频干扰监测:实验室原型
Sensors (Basel). 2024 Jan 13;24(2):508. doi: 10.3390/s24020508.
2
A Two-Stage Interference Suppression Scheme Based on Antenna Array for GNSS Jamming and Spoofing.基于天线阵列的 GNSS 干扰和欺骗的两级干扰抑制方案。
Sensors (Basel). 2019 Sep 7;19(18):3870. doi: 10.3390/s19183870.
3
GNSS interference and spoofing dataset.全球导航卫星系统干扰与欺骗数据集。
Data Brief. 2024 Mar 8;54:110302. doi: 10.1016/j.dib.2024.110302. eCollection 2024 Jun.
4
Performance Evaluation of CentiSpace Navigation Augmentation Experiment Satellites.星基增强系统实验卫星性能评估
Sensors (Basel). 2023 Jun 19;23(12):5704. doi: 10.3390/s23125704.
5
Collaborative Solutions for Interference Management in GNSS-Based Aircraft Navigation.基于全球导航卫星系统(GNSS)的飞机导航中干扰管理的协作解决方案。
Sensors (Basel). 2020 Jul 22;20(15):4085. doi: 10.3390/s20154085.
6
Recent Advances on Jamming and Spoofing Detection in GNSS.全球导航卫星系统中干扰与欺骗检测的最新进展
Sensors (Basel). 2024 Jun 28;24(13):4210. doi: 10.3390/s24134210.
7
Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver.利用多天线快照接收机进行盲欺骗 GNSS 星座检测。
Sensors (Basel). 2019 Dec 10;19(24):5439. doi: 10.3390/s19245439.
8
GNSS spoofing detection using a maximum likelihood-based sliding window method.基于最大似然的滑动窗口方法的 GNSS 欺骗检测。
PLoS One. 2020 Aug 28;15(8):e0237146. doi: 10.1371/journal.pone.0237146. eCollection 2020.
9
Characterization of the GNSS RFI Threat to DFMC GBAS Signal Bands.全球导航卫星系统射频干扰对差分多径改正全球定位系统地基增强系统信号频段的威胁特性分析
Sensors (Basel). 2022 Nov 8;22(22):8587. doi: 10.3390/s22228587.
10
Structure and Performance Analysis of Signal Acquisition and Doppler Tracking in LEO Augmented GNSS Receiver.低轨增强全球导航卫星系统接收机中信号采集与多普勒跟踪的结构与性能分析
Sensors (Basel). 2021 Jan 13;21(2):525. doi: 10.3390/s21020525.

引用本文的文献

1
COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location.基于CNN-BiLSTM-注意力机制的COSMIC-2射频干扰预测模型用于干扰检测与定位
Sensors (Basel). 2024 Dec 4;24(23):7745. doi: 10.3390/s24237745.

本文引用的文献

1
Computational Load Analysis of a Galileo OSNMA-Ready Receiver for ARM-Based Embedded Platforms.基于 ARM 的嵌入式平台上的伽利略 OSNMA 就绪接收器的计算负载分析。
Sensors (Basel). 2021 Jan 11;21(2):467. doi: 10.3390/s21020467.
2
Jammer Classification in GNSS Bands Via Machine Learning Algorithms.基于机器学习算法的全球导航卫星系统频段干扰分类。
Sensors (Basel). 2019 Nov 6;19(22):4841. doi: 10.3390/s19224841.
3
BeiDou Augmented Navigation from Low Earth Orbit Satellites.低地球轨道卫星的北斗增强导航。
Sensors (Basel). 2019 Jan 7;19(1):198. doi: 10.3390/s19010198.
4
Comparison of L1 and L5 Bands GNSS Signals Acquisition.L1 和 L5 波段 GNSS 信号获取的比较。
Sensors (Basel). 2018 Aug 23;18(9):2779. doi: 10.3390/s18092779.
5
A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.一种基于孪生支持向量机方法的全球导航卫星系统实时干扰监测技术
Sensors (Basel). 2016 Mar 4;16(3):329. doi: 10.3390/s16030329.