• 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

一种用于基于地球静止GNSS的双基地前视合成孔径雷达的基于傅里叶变换的成像算法。

A Fourier-Based Image Formation Algorithm for Geo-Stationary GNSS-Based Bistatic Forward-Looking Synthetic Aperture Radar.

作者信息

Zeng Zhangfan, Shi Zhiming, Xing Sainan, Pan Yongcai

机构信息

School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China.

出版信息

Sensors (Basel). 2019 Apr 26;19(9):1965. doi: 10.3390/s19091965.

DOI:10.3390/s19091965
PMID:31035450
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6539958/
Abstract

A Geo-Stationary GNSS-based Bistatic Forward-Looking Synthetic Aperture Radar (GeoSta-GNSS-BFLSAR) system is a particular kind of passive bistatic SAR system. In this system, a geo-stationary GNSS is used as the transmitter, while the receiver is deployed on a moving aircraft, which travels towards a target in a straight line. It is expected that such a radar system has potential for self-landing, self-navigation and battlefield information acquisition applications, etc. Up to now, little information from a research perspective can be found about GeoSta-GNSS-BFLSAR systems. To address this information gap, this paper proposes a preliminary image formation algorithm for GeoSta-GNSS-BFLSAR. The full details of the mathematical derivation are given. It is highlighted that, to overcome the long dwell time and spatial variance of GeoSta-GNSS-BFLSAR, a modified migration correction factor must be designed. In addition, the system performances and technical limitations of GeoSta-GNSS-BFLSAR such as focusing depth and spatial resolution are analytically discussed. In the end, a set of simulations including the image formation algorithm, focusing depth and spatial resolution were conducted for verification. It is demonstrated that the focusing performances of the proposed algorithm have a high level of similarity with the theoretical counterparts. This article thus proves the feasibility of GeoSta-GNSS-BFLSAR systems from a simulation level and establishes a foundation for the real applications of such a radar scheme in the future.

摘要

基于地球静止全球导航卫星系统的双基地前视合成孔径雷达（GeoSta-GNSS-BFLSAR）系统是一种特殊的无源双基地合成孔径雷达系统。在该系统中，地球静止全球导航卫星系统用作发射机，而接收机部署在一架直线飞向目标的移动飞机上。预计这样的雷达系统在自动着陆、自主导航和战场信息获取等应用方面具有潜力。到目前为止，从研究角度来看，关于GeoSta-GNSS-BFLSAR系统的信息很少。为了填补这一信息空白，本文提出了一种针对GeoSta-GNSS-BFLSAR的初步成像算法。给出了数学推导的全部细节。需要强调的是，为了克服GeoSta-GNSS-BFLSAR的长驻留时间和空间变化，必须设计一个修正的偏移校正因子。此外，还对GeoSta-GNSS-BFLSAR的系统性能和技术局限性，如聚焦深度和空间分辨率进行了分析讨论。最后，进行了一组包括成像算法、聚焦深度和空间分辨率的仿真以进行验证。结果表明，所提算法的聚焦性能与理论结果具有高度相似性。本文从而从仿真层面证明了GeoSta-GNSS-BFLSAR系统的可行性，并为该雷达方案未来的实际应用奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/2a1a1a351180/sensors-19-01965-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/e84bb572f2e2/sensors-19-01965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/ecd446c18e48/sensors-19-01965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/24a473e87939/sensors-19-01965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/d15ba8289a22/sensors-19-01965-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/769cbd777316/sensors-19-01965-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/faf55eeb7f32/sensors-19-01965-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/09b1e401e107/sensors-19-01965-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/7e43baa1732f/sensors-19-01965-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/2a1a1a351180/sensors-19-01965-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/e84bb572f2e2/sensors-19-01965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/ecd446c18e48/sensors-19-01965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/24a473e87939/sensors-19-01965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/d15ba8289a22/sensors-19-01965-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/769cbd777316/sensors-19-01965-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/faf55eeb7f32/sensors-19-01965-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/09b1e401e107/sensors-19-01965-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/7e43baa1732f/sensors-19-01965-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08e4/6539958/2a1a1a351180/sensors-19-01965-g009.jpg

相似文献

1
A Fourier-Based Image Formation Algorithm for Geo-Stationary GNSS-Based Bistatic Forward-Looking Synthetic Aperture Radar.一种用于基于地球静止GNSS的双基地前视合成孔径雷达的基于傅里叶变换的成像算法。
Sensors (Basel). 2019 Apr 26;19(9):1965. doi: 10.3390/s19091965.
2
A Novel General Imaging Formation Algorithm for GNSS-Based Bistatic SAR.一种基于全球导航卫星系统的双基地合成孔径雷达的新型通用成像算法。
Sensors (Basel). 2016 Feb 26;16(3):294. doi: 10.3390/s16030294.
3
Focusing Bistatic Forward-looking Synthetic Aperture Radar Based on An Improved Hyperbolic Range Model and A Modified Omega-K algorithm.基于改进双曲线距离模型和修正欧米伽 - K 算法的聚焦双基地前视合成孔径雷达
Sensors (Basel). 2019 Sep 1;19(17):3792. doi: 10.3390/s19173792.
4
An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS.一种基于Keystone变换和优化非均匀相位补偿的双基地前视SAR自适应动目标成像方法。
Sensors (Basel). 2017 Jan 23;17(1):216. doi: 10.3390/s17010216.
5
A Novel Range Compression Algorithm for Resolution Enhancement in GNSS-SARs.一种用于增强GNSS-SAR分辨率的新型距离压缩算法。
Sensors (Basel). 2017 Jun 25;17(7):1496. doi: 10.3390/s17071496.
6
An Improved RD Algorithm for Maneuvering Bistatic Forward-Looking SAR Imaging with a Fixed Transmitter.一种用于固定发射机的机动双基地前视SAR成像的改进RD算法。
Sensors (Basel). 2017 May 19;17(5):1152. doi: 10.3390/s17051152.
7
Focusing Bistatic FMCW SAR Signal by Range Migration Algorithm Based on Fresnel Approximation.基于菲涅尔近似的距离徙动算法聚焦双基地调频连续波合成孔径雷达信号
Sensors (Basel). 2015 Dec 21;15(12):32123-37. doi: 10.3390/s151229910.
8
Improved GNSS-Based Bistatic SAR Using Multi-Satellites Fusion: Analysis and Experimental Demonstration.基于多卫星融合的改进型全球导航卫星系统双基地合成孔径雷达：分析与实验演示
Sensors (Basel). 2020 Dec 11;20(24):7119. doi: 10.3390/s20247119.
9
Bistatic Forward-Looking SAR Moving Target Detection Method Based on Joint Clutter Cancellation in Echo-Image Domain with Three Receiving Channels.基于三接收通道回波图像域联合杂波对消的双基地前视合成孔径雷达动目标检测方法。
Sensors (Basel). 2018 Nov 8;18(11):3835. doi: 10.3390/s18113835.
10
Prototyping a GNSS-Based Passive Radar for UAVs: An Instrument to Classify the Water Content Feature of Lands.为无人机原型设计一种基于全球导航卫星系统的无源雷达：一种用于分类陆地含水量特征的仪器。
Sensors (Basel). 2015 Nov 10;15(11):28287-313. doi: 10.3390/s151128287.

引用本文的文献

1
Pulsar Emissions, Signal Modeling and Passive ISAR Imaging.脉冲星辐射、信号建模与被动逆合成孔径雷达成像
Sensors (Basel). 2019 Jul 30;19(15):3344. doi: 10.3390/s19153344.