Bistatic SAR moving target imaging algorithm study based on GPS-R signal
- Vol. 26, Issue 12, Pages: 2555-2567(2022)
Published： 07 December 2022
DOI： 10.11834/jrs.20210378
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摘要
全球导航卫星系统反射GNSS-R（Global Navigation Satellite System Reflectometry）用做双基地合成孔径雷达（简称GNSS-SAR）是近年来新兴的研究方向。当前GNSS-SAR成像的主要对象是地表静态目标，关于动目标成像的研究非常少。本文以全球定位系统GPS（Global Positioning System）卫星作为照射源，提出一种动目标成像方法。该方法首先构建了成像场景的双基地几何模型，推导了一种近似的双基地距离历程，用于描述目标回波的方位向相位变化，然后，采用Keystone变换校正由目标运动引起的未知距离单元徙动；组合短时傅里叶变换和随机抽样一致性算法估计未知的目标移动速度，最后，推导了方位向匹配滤波器用于方位向压缩从而完成动目标成像。采集了两组现场实验数据验证本文方法，实验结果表明：提出的算法能够有效地成像移动目标并且准确地估计目标运动速度、船长度、离岸垂直距离以及移动方向。
Abstract
Global navigation satellite system reflectometry (GNSS-R) is a typical fusion application of the remote sensing and navigation technology and has become a potential research direction. The use of GNSS-R for constructing a passive bistatic synthetic aperture radar (called as GNSS-SAR) has drawn great attention from the research community in recent years. Current investigations on GNSS-SAR focus on the static objects on land. However
few contributions to the moving target imaging can be found in this novel field. Imaging moving target is a long-standing subject for modern SAR systems. However
traditional GNSS-SAR image formation algorithms cannot be directly applied to the moving target due to the unknown motion. Accordingly
the moving target will be smeared and shifted in the static SAR image. To extend the application of GNSS-SAR
this work selects the global positioning system satellite as the illuminator of opportunity and proposes a frequency domain-based moving target image formation algorithm that has a higher processing efficiency than the traditional time domain-based GNSS-SAR algorithm.
To image a moving target
frequency domain-based algorithm should solve three main problems: (1) The unknown range cell migration induced by the moving target should be corrected. (2) The velocity of the moving target should be estimated. (3) The azimuth compression derivation should be performed due to the bistatic acquisition geometry. To deal with the main problems
this work selects maritime moving ships as the targets of interest and constructs a bistatic acquisition geometry where the receiver and the satellite are stationary during the observation time. Meanwhile
the trajectory of the moving target perpendicular to the line of sight of the receiver antenna is used as a synthetic aperture. An approximate bistatic range history is first deduced to describe the azimuthal phase variation of the target signal based on the bistatic acquisition geometry. A keystone transform is then employed to address the unknown range cell migration
and a method based on short time Fourier transform and random sample consensus is proposed to estimate the velocity. Finally
a derivation of azimuth compression is conducted to accomplish the moving target imaging.
Field experiments were carried out to validate the proposed moving target image formation algorithm. Experimental results show that: (1) The proposed velocity estimation method can obtain the velocity in a low signal-to-noise ratio scene where the least square method cannot work. The fluctuations of the target complex reflectivity will affect the velocity estimation results due to the long observation time
causing errors. However
the errors between the estimated velocities from two groups of the experimental data and the ground truth do not exceed 0.6 m/s. (2) Two targets shown in the SAR image have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range axis and the ships’ length along the cross-range axis. (3) The designed azimuth matched filter can help in judging the target’s moving direction. Nonetheless
this capability will disappear with the quasi-monostatic configuration. Therefore
the feasibility of the proposed moving image formation algorithm has been confirmed.
The proposed algorithm can be used for monitoring the moving ship target and obtain the target’s velocity
length
vertical distance
and moving direction in the future.
关键词
GNSS-RGNSS-SAR动目标成像Keystone变换短时傅里叶变换随机抽样一致性算法
Keywords
GNSS-RGNSS-SARmoving target imagingKeystone transformshort time Fourier transformrandom sample consensus
references
Antoniou M and Cherniakov M. 2013. GNSS-based bistatic SAR: a signal processing view. EURASIP Journal on Advances in Signal Processing, 2013(1): 98-113 [DOI: 10.1186/1687-6180-2013-98http://dx.doi.org/10.1186/1687-6180-2013-98]
Antoniou M, Cherniakov M and Ma H. 2015. Space-surface bistatic synthetic aperture radar with navigation satellite transmissions: a review. Science China Information Sciences, 58(6): 1-20 [DOI: 10.1007/s11432-015-5334-6http://dx.doi.org/10.1007/s11432-015-5334-6]
Antoniou M, Saini R and Cherniakov M. 2007. Results of a space-surface bistatic SAR image formation algorithm. IEEE Transactions on Geoscience and Remote Sensing, 45(11): 3359-3371 [DOI: 10.1109/TGRS.2007.902124http://dx.doi.org/10.1109/TGRS.2007.902124]
Auger F, Chassande-Mottin É and Flandrin P. 2012. On phase-magnitude relationships in the short-time Fourier transform. IEEE Signal Processing Letters, 19(5): 267-270 [DOI: 10.1109/LSP.2012.2190279http://dx.doi.org/10.1109/LSP.2012.2190279]
Bao Z, Xing M D and Wang T. 2005. Radar Imaging Techniques. Beijing: Electronics Industry Press
保铮, 刑孟道, 王彤. 2005. 雷达成像技术. 北京：电子工业出版社
Beltramonte T, Braca P, Bisceglie M D, Simone A D, Galdi C, Iodice A, Millefiori L M, Riccio D and Willett P. 2020. Simulation-based feasibility analysis of ship detection using GNSS-R delay-Doppler maps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1385 - 1399 [DOI: 10.1109/JSTARS.2020.2970221http://dx.doi.org/10.1109/JSTARS.2020.2970221]
Chen R Z, Wang L, Li D R, Chen L and Fu W J. 2019. A survey on the fusion of the navigation and the remote sensing techniques. Acta Geodaetica et Cartographica Sinica, 48(12): 1507-1522
陈锐志, 王磊, 李德仁, 陈亮, 付文举. 2019. 导航与遥感技术融合综述. 测绘学报, 48(12): 1507-1522 [DOI: 10.11947/j.AGCS.2019.20190446http://dx.doi.org/10.11947/j.AGCS.2019.20190446]
Cumming I G and Wong F H. 2005. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Norwood, MA: Artech House Press
Fischler M A and Bolles R C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381-395 [DOI: 10.1145/358669.358692http://dx.doi.org/10.1145/358669.358692]
He Z Y, Chen W and Yang Y. 2020. A hybrid integration method for moving target detection with GNSS-based passive radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 1184-1193 [DOI: 10.1109/JSTARS.2020.3037200http://dx.doi.org/10.1109/JSTARS.2020.3037200]
He Z Y, Chen W and Yang Y. 2020. GPS-based space-surface passive bistatic radar technique for maritime moving target detection. Acta Geodaetica et Cartographica Sinica, 49(12): 1523-1534
何振宇, 陈武, 杨扬. 2020. GPS天—地无源双基地雷达探测海面移动目标. 测绘学报, 49(12): 1523-1534 [DOI: 10.11947/j.AGCS.2020.20190487http://dx.doi.org/10.11947/j.AGCS.2020.20190487]
Kulpa J S, Malanowski M, Gromek D, Samczyński P, Kulpa K, and Gromek A. 2013. Experimental results of high-resolution ISAR imaging of ground-moving vehicles with a stationary FMCW radar. International Journal of Electronics and Telecommunications, 59(3): 293-299[DOI: 10.2478/eletel-2013-0035http://dx.doi.org/10.2478/eletel-2013-0035]
Liu F, Antoniou M, Zeng Z and Cherniakov M. 2013. Point spread function analysis for BSAR with GNSS transmitters and long dwell times: theory and experimental confirmation. IEEE Geoscience and Remote Sensing Letters, 10(4): 781-785 [DOI: 10.1109/LGRS.2012.2223655http://dx.doi.org/10.1109/LGRS.2012.2223655]
Liu F F, Fan X Z, Zhang L Z, Zhang T and Liu Q H. 2019. GNSS-based SAR for urban area imaging: topology optimization and experimental confirmation. International Journal of Remote Sensing, 40(12): 4668-4682 [DOI: 10.1080/01431161.2019.1569790http://dx.doi.org/10.1080/01431161.2019.1569790]
Liu L D, Yuan W M, Wu S J and Sun X W. 2004. Bistatic radar system based on GPS illumination. Chinese Journal of Radio Science, 19(1): 109-113
刘立东, 袁伟明, 吴顺君, 孙晓闻. 2004. 基于GPS照射源的天地双基地雷达探测系统. 电波科学学报, 19(1): 109-113 [DOI: 10.3969/j.issn.1005-0388.2004.01.024http://dx.doi.org/10.3969/j.issn.1005-0388.2004.01.024]
Ma H, Antoniou M and Cherniakov M. 2015. Passive GNSS-based SAR resolution improvement using joint Galileo E5 signals. IEEE Geoscience and Remote Sensing Letters, 12(8): 1640-1644 [DOI: 10.1109/LGRS.2015.2417594http://dx.doi.org/10.1109/LGRS.2015.2417594]
Ma H, Antoniou M, Pastina D, Santi F, Pieralice F, Bucciarelli M and Cherniakov M. 2018. Maritime moving target indication using passive GNSS-based bistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 54(1): 115-130 [DOI: 10.1109/TAES.2017.2739900http://dx.doi.org/10.1109/TAES.2017.2739900]
Ozaktas H M, Arikan O, Kutay M A and Bozdagt G. 1996. Digital computation of the fractional Fourier transform. IEEE Transactions on Signal Processing, 44(9): 2141-2150 [DOI: 10.1109/78.536672http://dx.doi.org/10.1109/78.536672]
Pastina D, Santi F, Pieralice F, Bucciarelli M, Ma H, Tzagkas D, Antoniou M and Cherniakov M. 2018. Maritime moving target long time integration for GNSS-based passive bistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 54(6): 3060-3083 [DOI: 10.1109/TAES.2018.2840298http://dx.doi.org/10.1109/TAES.2018.2840298]
Peng X F, Wan W, Li F and Chen X W. 2017. The suitability analysis of soil moisture retrieval using GNSS-R technology. Journal of Remote Sensing, 21(3): 341-350
彭学峰, 万玮, 李飞, 陈秀万. 2017. GNSS-R土壤水分遥感的适宜性分析. 遥感学报, 21(3): 341-350 [DOI: 10.11834/jrs.20176198http://dx.doi.org/10.11834/jrs.20176198]
Perry R P, Dipietro R C and Fante R L. 1999. SAR imaging of moving targets. IEEE Transactions on Aerospace and Electronic Systems, 35(1): 188-200 [DOI: 10.1109/7.745691http://dx.doi.org/10.1109/7.745691]
Wan W, Chen X W, Peng X F, Bai W H, Xia J M, Liang H, Zhang X M, Xiong P, Yang T, Cao Y C, Yin C, Zhao L M and Hong Y. 2016. Overview and outlook of GNSS remote sensing technology and applications. Journal of Remote Sensing, 20(5): 858-874
万玮, 陈秀万, 彭学峰, 白伟华, 夏俊明, 梁宏, 张学民, 熊攀, 杨婷, 曹云昌, 尹聪, 赵利民, 洪阳. 2016. GNSS遥感研究与应用进展和展望. 遥感学报, 20(5): 858-874 [DOI: 10.11834/jrs.20166228http://dx.doi.org/10.11834/jrs.20166228]
Wang B, Zhu Y L, Gao C Q and Wu S Y. 2019. Discussion on imaging technology of GNSS-R bistatic SAR moving target. Journal of Navigation and Positioning, 7(1): 59-64
王博, 朱云龙, 高超群, 吴世玉. 2019. GNSS-R双基SAR运动目标成像技术探讨. 导航定位学报, 7(1): 59-64 [DOI: 10.16547/j.cnki.10-1096.20190111http://dx.doi.org/10.16547/j.cnki.10-1096.20190111]
Wang J and Zhao Y B. 2011. Research on implementation of Keystone transform. Fire Control Radar Technology, 40(1): 45-51
王娟, 赵永波. 2011. Keystone变换实现方法研究. 火控雷达技术, 40(1): 45-51 [DOI: 10.3969/j.issn.1008-8652.2011.01.010http://dx.doi.org/10.3969/j.issn.1008-8652.2011.01.010]
Wang X L, He X F, Chen S, Zhang Q and Song M F. 2021. Preliminary study on theory and method of ground-based GNSS-IR wind speed. Acta Geodaetica et Cartographica Sinica, 50(10): 1298-1307
王笑蕾, 何秀凤, 陈殊, 张勤, 宋敏峰. 2021. 地基GNSS-IR风速反演原理及方法初探. 测绘学报, 50(10): 1298-1307 [DOI: 10.11947/j.AGCS.2021.20200586http://dx.doi.org/10.11947/j.AGCS.2021.20200586]
Wang Z M, Liu Z K, An J C and Lin G B. 2018. Snow depth detection and error analysis derived from SNR of GPS and BDS. Acta Geodaetica et Cartographica Sinica, 47(1): 8-16
王泽民, 刘智康, 安家春, 林国标. 2018. 基于GPS和北斗信噪比观测值的雪深反演及其误差分析. 测绘学报, 47(1): 8-16 [DOI: 10.11947/j.AGCS.2018.20160644http://dx.doi.org/10.11947/j.AGCS.2018.20160644]
Wood J C and Barry D T. 1994. Radon transformation of time-frequency distributions for analysis of multicomponent signals. IEEE Transactions on Signal Processing, 42(11): 3166-3177 [DOI: 10.1109/78.330375http://dx.doi.org/10.1109/78.330375]
Wu X, Xia J, Jin S, Bai W and Dong Z. 2019. Is soil salinity detectable by GNSS-R/IR?//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Yokohama: IEEE: 6227-6230 [DOI: 10.1109/IGARSS.2019.8898902http://dx.doi.org/10.1109/IGARSS.2019.8898902]
Xia J M, Wu X R, Bai W H, Sun Y Q, Luo L M, Du Q F, Wang X Y, Liu C L, Meng X G, Zhao D Y and Wang Y Q. 2019. Research on the effects of delay and Doppler intervals on GNSS-R DDM simulation. Journal of Remote Sensing, 23(3): 456–463
夏俊明, 吴学睿, 白伟华, 孙越强, 骆黎明, 杜起飞, 王先毅, 柳聪亮, 孟祥广, 赵丹阳, 王迎强. 2019. GNSS-R DDM波形仿真的时延与多普勒间隔研究. 遥感学报, 23(3): 456-463 [DOI: 10.11834/jrs.20198018http://dx.doi.org/10.11834/jrs.20198018]
Xia X G. 2000. Discrete chirp-Fourier transform and its application to chirp rate estimation. IEEE Transactions on Signal Processing, 48(11): 3122-3133 [DOI: 10.1109/78.875469http://dx.doi.org/10.1109/78.875469]
Xie J P, Bertinoa L, Cardellachb E, Semmlingd M, Wickert J. 2018. An OSSE evaluation of the GNSS-R altimetry data for the GEROS-ISS mission as a complement to the existing observational networks. Remote Sensing of Environment, 209: 152-165 [DOI: 10.1016/j.rse.2018.02.053http://dx.doi.org/10.1016/j.rse.2018.02.053]
Yang Y, Zheng Y, Yu W K, Chen W and Weng D J. 2019. Deformation monitoring using GNSS-R technology. Advances in Space Research, 63(10): 3303-3314 [DOI: 10.1016/j.asr.2019.01.033http://dx.doi.org/10.1016/j.asr.2019.01.033]
Yang Z Q, Zhang Y S and Luo Y J. 1998. Bastatic(Multistatic) Radar Systems. Beijing: National Defense Industry Press
杨振起, 张永顺, 骆永军. 1998. 双(多)基地雷达系统. 北京: 国防工业出版社
Yu K G, Wang S Y, Li Y W, Chang X and Li J C. 2019. Snow depth estimation with GNSS-R dual receiver observation. Remote Sensing, 11(17): 2056-2072 [DOI: 10.3390/rs11172056http://dx.doi.org/10.3390/rs11172056]
Zeng H C, Wang P B, Chen J, Liu W, Ge L L and Yang W. 2016. A novel general imaging formation algorithm for GNSS-based bistatic SAR. Sensors, 16(3): 294 [DOI: 10.3390/s16030294http://dx.doi.org/10.3390/s16030294]
Zeng Z F. 2013. Passive Bistatic SAR with GNSS Transmitter and a Stationary Receiver. Birmingham: University of Birmingham: 90-91
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