微型合成孔径雷达成像信号处理技术

朱岱寅; 张营; 俞翔; 毛新华; 张劲东; 李勇

doi:10.12000/JR19094

微型合成孔径雷达成像信号处理技术

DOI: 10.12000/JR19094

朱岱寅^1, ,,
张营^1,,
俞翔^2,,
毛新华^1,,
张劲东^1,,
李勇^1,

1.
南京航空航天大学电子信息工程学院雷达成像与微波光子技术教育部重点实验室南京 211106
2.
南京工程学院计算机工程学院南京 211167

基金项目: 国家自然科学基金(61671240)，航空科学基金(20182052013)

详细信息

作者简介:
朱岱寅(1974–)，男，江苏人，教授，博士生导师。研究方向为合成孔径雷达/逆合成孔径雷达(SAR/ISAR)成像以及自聚焦算法、干涉SAR成像、SAR地面动目标指示以及机载雷达动目标指示技术。自2018年开始担任《雷达学报》编委。E-mail: zhudy@nuaa.edu.cn

张　营(1994–)，男，河北人，博士生。研究方向为视频合成孔径雷达、计算机视觉与图像解译、机器学习与遥感、SAR成像技术。E-mail: zhy1994@nuaa.edu.cn

俞　翔(1982–)，男，江苏人，副教授。研究方向为逆合成孔径雷达成像技术。E-mail: cw-al20@163.com

毛新华(1979–)，男，副教授，硕士生导师。研究方向为机载/星载合成孔径雷达、SAR自聚焦技术、微型无人机雷达成像、动目标成像。E-mail: xinhua@nuaa.edu.cn

张劲东(1981–)，男，江苏人，副教授，硕士生导师。研究方向为新体制雷达的研发、雷达信号分析与处理、高速数字信号处理系统设计与实现。E-mail: zjdjs@126.com

李　勇(1977–)，男，河南人，副教授，硕士生导师。研究方向为雷达信号处理、雷达系统。E-mail: limack@nuaa.edu.cn

通讯作者:
朱岱寅 zhudy@nuaa.edu.cn

中图分类号: TN957.5
计量
- 文章访问数: 4134
- HTML全文浏览量: 1901
- PDF下载量: 549
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-28
- 修回日期: 2019-12-24
- 网络出版日期: 2019-12-01

Imaging Signal Processing Technology for Miniature Synthetic Aperture Radar

ZHU Daiyin^{1
, ,},
ZHANG Ying^1
,,
YU Xiang^2
,,
MAO Xinhua^1
,,
ZHANG Jindong^1
,,
LI Yong^1
,

1.
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Department of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Funds: The National Natural Science Foundation of China (61671240), The Aeronautical Science Foundation of China (20182052013)

More Information

Corresponding author: ZHU Daiyin, zhudy@nuaa.edu.cn

摘要

摘要: 微型合成孔径雷达(MiniSAR)有效突破了时间与空间的限制，具备轻量化、低功耗、高灵活度等优势，能够满足感兴趣区域(ROI)的高分辨成像需求。然而，MiniSAR成像信号处理依然面临若干技术难题，例如复杂航迹条件下对地面目标的高分辨成像，非合作动目标的重聚焦，数据处理的效率与实时性等。据此，该文提出了一系列成像信号处理技术及其对应的现场可编辑门阵列(FPGA)硬件设计架构，从而实现了MiniSAR高分辨率成像与实时性处理。最后，基于多组聚束/条带式MiniSAR试验结果，验证了该文方法的有效性和可靠性。
- 微型合成孔径雷达 /
- 高分辨率 /
- 成像信号处理 /
- 实时处理
Abstract: Miniature Synthetic Aperture Radar (MiniSAR) has been making breakthroughs to effectively overcome the limitations of time and space, and has exhibited superiority with respect to light weight and low-power consumption as well as high flexibility to achieve high-resolution imaging for the Region Of Interest (ROI). However, imaging signal processing for MiniSAR systems still face several technical challenges such as high-resolution imaging of ground targets under complicated trajectories, refocusing of non-cooperative moving targets, and efficient and real-time processing of echo data. In this paper, a series of imaging signal processing and associated hardware designs using Field-Programmable Gate Array (FPGA) architecture have been proposed to realize MiniSAR ultra-high-resolution imaging and real-time processing. Additionally, experimental results utilizing considerable spotlight and stripmap MiniSAR data have been presented to demonstrate the effectiveness and reliability of the proposed technology.
- Miniature Synthetic Aperture Radar (MiniSAR) /
- High resolution /
- Imaging signal processing /
- Real-time processing

HTML全文

图 1 所提MiniSAR成像信号处理流程图

Figure 1. Proposed flow diagram of MiniSAR imaging signal processing

下载: 全尺寸图片幻灯片

图 2 基于FPGA设计架构的MiniSAR成像处理系统

Figure 2. MiniSAR imaging processing system based on FPGA design architecture

下载: 全尺寸图片幻灯片

图 3 PFA 2维处理模块

Figure 3. 2-D processing modules of PFA

下载: 全尺寸图片幻灯片

图 4 相位梯度自聚焦模块结构图

Figure 4. Structure diagram of phase gradient autofocus module

下载: 全尺寸图片幻灯片

图 5 直接式分段存储

Figure 5. Directly segmented storage

下载: 全尺寸图片幻灯片

图 6 MiniSAR试验系统

Figure 6. MiniSAR experimental system

下载: 全尺寸图片幻灯片

图 7 ROI#1成像处理与聚焦对比

Figure 7. Imaging processing and focusing comparison for ROI#1

下载: 全尺寸图片幻灯片

图 8 聚束式实测数据ROI#2和ROI#3处理结果

Figure 8. Measured data processing results for spotlight ROI#2 and ROI#3

下载: 全尺寸图片幻灯片

图 9 条带式实测数据ROI#4处理结果

Figure 9. Measured data processing result for stripmap ROI#4

下载: 全尺寸图片幻灯片

图 10 条带式实测数据ROI#5处理结果

Figure 10. Measured data processing result for stripmap ROI#5

下载: 全尺寸图片幻灯片

表 1 资源利用率

Table 1. Resource utilization

资源已用资源可用资源利用率(%)

Slice Registers 355480 866400 41
Slice LUTs 273549 433200 63
Block RAM/FIFO 501 1470 34
DSP48E1s 1204 3600 33

下载: 导出CSV

表 2 主要的系统参数

Table 2. Main system parameters

系统参数数值

带宽(GHz) 1.8
载频(GHz) 9.7
飞行速度(m/s) 5
脉冲宽度(ms) 4
数据采样率(MHz) 50
脉冲重复频率(Hz) 250

下载: 导出CSV

参考文献(14)

[1]	MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
[2]	ZHANG Ying and ZHU Daiyin. Height retrieval in postprocessing-based VideoSAR image sequence using shadow information[J]. IEEE Sensors Journal, 2018, 18(19): 8108–8116. doi: 10.1109/JSEN.2018.2865112
[3]	王岩飞, 刘畅, 詹学丽, 等. 无人机载合成孔径雷达系统技术与应用[J]. 雷达学报, 2016, 5(4): 333–349. doi: 10.12000/JR16089 WANG Yanfei, LIU Chang, ZHAN Xueli, et al. Technology and applications of UAV synthetic aperture radar system[J]. Journal of Radars, 2016, 5(4): 333–349. doi: 10.12000/JR16089
[4]	丁满来, 丁赤飚, 唐跞, 等. 一种W波段无人机微型SAR系统[J]. 电子与信息学报, 2019, 41(4): 1939–1945. doi: 10.11999/JEIT180946 DING Manlai, DING Chibiao, TANG Li, et al. A W band Mini-SAR system for unmanned aerial vehicle[J]. Journal of Electronics &Information Technology, 2019, 41(4): 1939–1945. doi: 10.11999/JEIT180946
[5]	ZHOU Song, YANG Lei, ZHAO Lifan, et al. Quasi-polar-based FFBP algorithm for miniature UAV SAR imaging without navigational data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7053–7065. doi: 10.1109/TGRS.2017.2739133
[6]	CASALINI E, FRIOUD M, SMALL D, et al. Refocusing FMCW SAR moving target data in the wavenumber domain[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6): 3436–3449. doi: 10.1109/TGRS.2018.2884830
[7]	ZHAO Lifan, WANG Lu, BI Guoan, et al. An autofocus technique for high-resolution inverse synthetic aperture radar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6392–6403. doi: 10.1109/TGRS.2013.2296497
[8]	VEHMAS R, JYLHÄ J, VÄILÄ M, et al. Data-driven motion compensation techniques for noncooperative ISAR imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 295–314. doi: 10.1109/TAES.2017.2756518
[9]	ZHU Daiyin, YE Shaohua, and ZHU Zhaoda. Polar format agorithm using chirp scaling for spotlight SAR image formation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(4): 1433–1448. doi: 10.1109/TAES.2008.4667720
[10]	MARTORELLA M, GIUSTI E, BERIZZI F, et al. ISAR based techniques for refocusing non-cooperative targets in SAR images[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 332–340. doi: 10.1049/iet-rsn.2011.0310
[11]	ZHU Daiyin, WANG Ling, YU Yusheng, et al. Robust ISAR range alignment via minimizing the entropy of the average range profile[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 204–208. doi: 10.1109/LGRS.2008.2010562
[12]	俞翔, 朱岱寅. 一种改进型全局最小熵ISAR距离对准算法[J]. 数据采集与处理, 2012, 27(5): 535–540. doi: 10.3969/j.issn.1004-9037.2012.05.003 YU Xiang and ZHU Daiyin. Improved global minimum entropy range alignment algorithm for ISAR[J]. Journal of Data Acquisition &Processing, 2012, 27(5): 535–540. doi: 10.3969/j.issn.1004-9037.2012.05.003
[13]	ZHU Daiyin, JIANG Rui, MAO Xinhua, et al. Multi-subaperture PGA for SAR autofocusing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 468–488. doi: 10.1109/taes.2013.6404115
[14]	郭江哲, 朱岱寅, 毛新华. 一种SAR两维自聚焦算法的FPGA实现[J]. 雷达学报, 2016, 5(4): 444–452. doi: 10.12000/JR15092 GUO Jiangzhe, ZHU Daiyin, and MAO Xinhua. FPGA implementation of a SAR two-dimensional autofocus approach[J]. Journal of Radars, 2016, 5(4): 444–452. doi: 10.12000/JR15092