<p>作为高级驾驶辅助系统核心之一的汽车毫米波雷达因其具有全天时、全天候成像等优势，逐渐成为学者关注的焦点。但由于角度分辨率受到实际天线孔径的限制，导致成像效果较差。合成孔径雷达（Synthetic Aperture Radar, SAR）利用较小的天线实孔径合成大虚拟孔径，弥补了传统毫米波雷达的不足，且可以获得二维高分辨图像。大斜视车载SAR成像在交通安全中发挥着重要作用，例如提前感知斜前方场景中的目标有助于避免交通事故的发生，更适用于环境感知的实时性要求。为此，本文针对大斜视车载SAR成像算法展开研究，提出了一种基于级数反演法的大斜视车载SAR实现方法，同时对非理想运动下的运动补偿(Motion Correction, MOCO)问题展开研究。主要研究内容如下：</p> <p>针对车载SAR成像计算复杂度高的难题，本文采用级数反演的方法，对大斜视成像算法展开研究。首先，构建大斜视场景车载SAR成像几何模型，进而对斜距表达式进行高阶泰勒级数展开；接着，深入分析了由展开项所引入的相位误差，并利用驻定相位法推导距离多普勒域信号的精确表达式；随后，对距离维分块进行处理。对距离维分块的目的是为了解决大斜视车载SAR回波数据的场景空变性问题，进而实现了对回波数据的精准聚焦。最后，为了验证该方法的优势和有效性，将仿真结果与传统的成像方法进行对比分析，利用积分旁瓣比、峰值旁瓣比和分辨率作为评判标准对本文算法与传统算法进行量化的对比，证实了该算法的可行性。</p> <p>针对车载SAR平台受道路状况和运动灵活性制约的问题，难以保持匀速直线运动，产生的非理想运动轨迹会严重降低成像质量。此外，复杂的运动轨迹会导致距离-方位维产生严重的交叉耦合，使得已有的运动补偿方法精度不够从而影响聚焦效果。因此，本文深入研究了基于惯导(Inertial Navigation System, INS)的MOCO技术。首先，对INS误差的精度展开分析，并构建车载SAR运动误差的模型；接着，分析运动误差带来的问题，尤其是垂直于运动方向的误差及其空变性问题；随后，对运动误差进行空不变相位和空变相位补偿，以优化SAR图像的聚焦性能；最后，通过搭建外场实验装置的方式对实测结果进行对比分析，验证了该方法的有效性。</p>

<p>As one of the core of advanced driver assistance system, automotive millimeter wave radar has gradually become the focus of scholars because of its advantages of all-day and all-weather imaging. However, because the angular resolution is limited by the actual antenna aperture, the imaging effect is poor. Synthetic Aperture Radar (SAR) makes up for the shortage of traditional millimeter wave radar by synthesizing a large virtual aperture from a small real aperture of the antenna, and can obtain two-dimensional high-resolution images. Large squint vehicular SAR imaging plays an important role in traffic safety. For example, it facilitates early perception of objects in oblique front-view scenes, contributing to the prevention of traffic accidents and better aligning with the real-time requirements of environmental perception. Therefore, research is conducted in this thesis on the imaging algorithm of large squint angle carborne Synthetic Aperture Radar (SAR)&nbsp;and proposes&nbsp;a method for its implementation based on the series inversion technique, while simultaneously investigating the problem of Motion Correction (MOCO) under non-ideal motion conditions. The main research contents are as follows:</p> <p>In this thesis, the challenge of high computational complexity in carborne SAR imaging is addressed by employing the method of series inversion to investigate the large squint imaging algorithm. Firstly, the geometric model of vehicle-mounted SAR imaging in large squint scene is constructed, and then the slant distance expression is expanded by high order Taylor series. Secondly, the phase error introduced by the expansion term is deeply analyzed, and the precise expression of the range Doppler signal is derived by using the standing phase method. Then the distance dimension is processed into blocks. Among them, the purpose of dividing the distance dimension is to solve the problem of the scene space variability of the large squint vehicle-mounted SAR echo data, and then realize the accurate focusing of the echo data. Finally, to verify the advantages and effectiveness of the proposed method, the simulation results were compared with traditional imaging methods, and the proposed algorithm was quantitatively compared with traditional algorithms using the criteria of Integrated Side-Lobe Ratio, Peak Side-Lobe Ratio&nbsp;and Impulse Resolution Width, which confirmed the feasibility of the proposed method.</p> <p>In this thesis,&nbsp;the vehicle-mounted SAR platform is restricted by road conditions and motion flexibility, it is difficult to maintain uniform linear motion, and the resulting non-ideal motion trajectory will seriously reduce the imaging quality. In addition, complex motion trajectories can cause serious cross-coupling between range-azimuth dimension, which makes the existing motion compensation methods less accurate and thus affects the focusing effect. Therefore, this paper deeply studies MOCO technology based on Inertial Navigation System (INS). Firstly, the accuracy of INS error is analyzed, and the vehicle SAR motion error model is built. Then, the problems caused by motion error are analyzed, especially the error perpendicular to the direction of motion and its variability. Then, the motion error is compensated by space-invariant phase and space-variant phase to optimize the focusing performance of SAR images. Finally, the effectiveness of the proposed method is verified by comparing and analyzing the measured results by setting up an outfield experimental device.</p>

[1]林倩,杨姝玥,刘林盛.浅析毫米波雷达在汽车电子中的应用[J].天津理工大学学报,2024,40(05):80-85.

[2]王士龙.浅谈直升机防撞雷达现状与技术发展[J].现代雷达,2024,46(05):9-12.

[3]王金生,江城城,陈风月.基于毫米波雷达在汽车尾部盲区中的应用研究[J].汽车与配件,2023,(23):66-68..

[4]Broggi A, Buzzoni M, Debattisti S, et al. Extensive tests of autonomous driving technologies[J]. IEEE Transactions on Intelligence Transportation Systems, 2013, 14(3): 1403-1415..

[5]Sun S, Petropulu A, Poor H. MIMO radar for advanced driver-assistance systems and autonomous driving: advantages and challenges[J]. IEEE Signal Processing Magazine, 2020, 37(4): 98-117.

[6]Yanik E M ,Wang D ,Torlak M .Development and Demonstration of MIMO-SAR mmWave Imaging Testbeds[J].IEEE Access,2020,8126019-126038.

[7]李高源,王晋宇,张长弓,等.SAR图像仿真方法研究综述[J].计算机工程与应用,2021,57(15):62-72.

[8]丁鹭飞,耿富录. 雷达原理[M]. 西安: 西安电子科技大学出版社, 2006.

[9]保铮,邢孟道,王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.

[10]黄岩,张慧,兰吕鸿康,等.汽车毫米波雷达信号处理技术综述[J].雷达学报,2023,12(05):923-970.

[11]Hu R, Rao B S M R, Murtada A, et al. Automotive squint-forward-looking SAR: High resolution and early warning[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(4): 904-912.

[12]王佳慧,杨果,王雨晗,等.基于MSER的车载毫米波雷达SAR图像目标检测[J].现代雷达,2024:1-10.

[13]黄岩,张慧,兰吕鸿康,等.汽车毫米波雷达信号处理技术综述[J].雷达学报,2023,12(05):923-970.

[14]王天云,刘冰,魏强,等.压缩感知成像雷达研究进展[J].电光与控制,2019,26(07):1-8.

[15]B. Walker, G. Sander, M. Thompson, B. Burns, R. Fellerhoff, and D. Dubbert, A high-resolution, four-band SAR testbed with real-time image formation[C]. in Proc. Int. Geosci. Remote Sens. Symp. (IGARSS), 1996: 1881-1885.

[16]Farhadi M, Feger R, Fink J, et al. Phase error estimation for automotive SAR[C]//2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). IEEE, 2020: 1-4.

[17]Tagliaferri D, Rizzi M, Tebaldini S, et al. Cooperative synthetic aperture radar in an urban connected car scenario[C]. 2021 1st IEEE International Online Symposium on Joint Communications & Sensing (JC&S). IEEE, 2021: 1-4.

[18]阚瀛芝,朱永锋,肖怀铁.车载毫米波SAR对近海域的成像及监测[J].现代电子技术,2012,35(18):122-124+132.

[19]Li Wei, Wang Jun.A New Improved Step Transform Algorithm for Highly Squint SAR Imaging[J]. IEEE Geoscience and Remote Sensing Society.2010, (99):118-112.

[20]Zheng Y, Guan J, Jiang G, et al. A modified algorithm for highly squinted missile-borne SAR imaging with large acceleration[J]. IEEE Access, 2024.

[21]Dong L, Han S, Zhu D, et al. A Modified Polar Format Algorithm for Highly Squinted Missile-Borne SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2023.

[22]Liu Y, Tao M, Shi T, et al. Sub-Aperture Polar Format Algorithm for Curved Trajectory Millimeter Wave Radar Imaging[J]. IEEE Transactions on Radar Systems, 2024.

[23]Han S, Zhu D, Mao X. Squint spotlight SAR imaging by two-step scaling transform-based extended PFA and 2-D autofocus[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 1295-1307.

[24]李宁,别博文,邢孟道,等.基于多普勒重采样的恒加速度大斜视SAR成像算法[J].电子与信息学报,2019,41(12):2873-2880.

[25]张延,郭苹,张林让,等.高分辨星载SAR的方位维频率非线性变标成像算法[J].系统工程与电子技术,2017,39(08):1716-1722.

[26]Guo Y, Wang P, Men Z, et al. A Hybrid Chirp Scaling Algorithm for Squint Sliding Spotlight SAR Data Imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 1-5.

[27]L. Zhang, H.-L. Li, Z.-J. Qiao, and Z.-W. Xu, A fast BP algorithm with wavenumber spectrum fusion for high-resolution spotlight SAR imaging[J]. IEEE Geosci. Remote Sens. Lett., 2018, 11(9): 1460-1464.

[28]Liang, Y., Li, G., Wen, J., Zhang, G., Dang, Y., Xing, M., A fast time-domain SAR imaging and corresponding autofocus method based on hybrid coordinate system[J]. IEEE Trans. Geosci. Remote Sens., 2019, 57(11): 8627-8640.

[29]张筱靖,何岷,贺志毅,等.弹载前斜视SAR BP成像算法与实现[J].现代防御技术,2017,45(06):48-53.

[30]C. Hu, Z. Liu, and T. Long, An improved CS algorithm based on the curved trajectory in geosynchronous SAR[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2012, 5(3): 795-808.

[31]彭靳,吴勇,宋红军.SAR大斜视RMA快速成像算法研究[J].计算机仿真.2013,30(03):398-402.

[32]Qian J,Zhou J,Ding Z,et al. Nondestructive Testing Based on Near Field THz SAR Imaging[C]//2023 24th International Vacuum Electronics Conference (IVEC). IEEE, 2023: 1-2.

[33]余小龙,张炎,陶小辉等. MIMO 雷达近场成像校准与互耦合补偿方法[J]. 雷达科学与技术，2023，21(4):420-430.

[34]Wu H, Zwick T. Automotive SAR for parking lot detection[C]2009 German microwave conference. March 16-18, 2009. Munich, Germany: IEEE, 2009: 1-8.

[35]Iqbal H, Sajjad M B, Mueller M, et al. SAR imaging in an automotive scenario[C]2015 IEEE 15th Mediterranean Microwave Symposium. November 30-December 2, 2015. Lecce, Italy: IEEE, 2015: 1-4.

[36]Kobayashi Y, Yamada H, Yamaguchi Y, et al. Fundamental study on data volume reduction for squint mode SAR with millimeter wave automotive radar by using compressed sensing[C]//2016 International Symposium on Antennas and Propagation (ISAP). IEEE, 2016: 322-323.

[37]邓钱钰,王纪平,郭淑婷,等.一种适用于星载大斜视聚束SAR的改进距离徙动算法[J].信号处理,2024,40(03):569-586.

[38]刘喜旺. FMCW SAR多模式成像算法研究[D].国防科学技术大学,2019.

[39]闫梦圆,高安琪,薛晨,等.基于BP和PGA算法的斜视SAR成像方法[J].中国传媒大学学报(自然科学版),2021,28(03):25-31.2021.03.004.

[40]薛鹏凤,郭苹.基于改进极坐标格式算法的大斜视SAR成像方法[J].空军工程大学学报(自然科学版),2022,23(03):82-88.

[41]王佳慧,刘衍琦,王纪平,等.基于改进PFA的车载大斜视SAR成像算法[J].雷达科学与技术,2023,21(04):411-419.

[42]孙波,李千龙,陈可正.机载SAR成像原理及运动补偿理论研究[J].电子制作,2023,31(21):108-111.

[43]张法桐,付耀文,杨威,等.微小型无人机载FMCW SAR宽波束运动补偿算法[J].系统工程与电子技术,2024:1-12.

[44]Zhen C ,Zhimin Z ,Yashi Z , et al.A Novel Motion Compensation Scheme for Airborne Very High Resolution SAR[J].Remote Sensing,2021,13(14):2729-2729.

[45]Chen J ,Liang B ,Yang D , et al.Two-Step Accuracy Improvement of Motion Compensation for Airborne SAR With Ultrahigh Resolution and Wide Swath[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(9):7148-7160.

[46]J. L. Farrel. Effection of navigation errors in maneuvering SAR. IEEE Trans. Aeros. and Electro. Syst. 1973, 9(5): 750-776.

[47]J. C. Kirk. Motion compensation for SAR. IEEE Trans. Aeros. and Electro. Syst. 1975, 11(3): 338-348.

[48]M. Manzoni et al., "Motion Estimation and Compensation in Automotive MIMO SAR," in IEEE Transact-ions on Intelligent Transportation Systems, vol. 24, no.2, pp.1756-1772, Feb. 2023.

[49]J. M. Merlo and J. A. Nanzer, "Multi-Pass Automotive Synthetic Aperture Radar Image Fusion," 2023 IEEE Radar Conference (RadarConf23), San Antonio,TX, USA, 2023, pp.1-6.

[50]M. Farhadi et al., "Space-variant Phase Error Estimation and Correction for Automotive SAR," 2020 17thEuropean Radar Conference (EuRAD), Utrecht, Netherlands, 2021, pp. 310-313.

[51]X. Wu and Z. Zhu, A novel autofocus algorithm based on minimum entropy criteria for SAR images[J]. Syst. Eng. Electron., 2003, 25(7): 867-869.

[52]Huang, D., Guo, X., Zhang, Z., Yu, W., Truong. T. K., Full-aperture azimuth spatial-variant autofocus based on contrast maximization for highly squinted synthetic aperture radar[J]. IEEE Trans. Geosci. Remote Sens., 2020, 58: 330-347.

[53]史姝赟.基于子带信号处理的太赫兹SAR运动误差补偿方法[J].信息对抗技术,2023,2(03):85-94.

[54]李谨成,郭德明.面向条带SAR的多孔径图像偏移自聚焦算法[J].雷达科学与技术,2020,18(05):551-556.

[55]郑义明,保铮.一种改进相位误差估计算法[J].西安电子科技大学学报,2001(04):472-477.

[56]张磊. 高分辨SAR/ISAR成像及误差补偿技术研究[D]. 西安电子科技大学, 2012.

[57]唐禹,邢孟道,徐宗志,徐兴旺. 一种在大斜视SAR成像模式下对多普勒调频率进行精确估计的方法[P]. 陕西：CN103675815A,2014-03-26.

[58]安道祥,冯东,陈乐平,黄晓涛,周智敏. 一种机载全息SAR成像中的相位校正方法[P]. 湖南省：CN109917389A,2019-06-21.

[59]Liu Y, Wang J, Bao Y, et al. Automotive millimeter wave SAR imaging in curved trajectory[C]. 2022 14th International Conference on Signal Processing Systems (ICSPS). IEEE, 2022: 493-495.

[60]Liu Y, Tao M, Shi T, et al. Sub-Aperture Polar Format Algorithm for Curved Trajectory Millimeter Wave Radar Imaging[J]. IEEE Transactions on Radar Systems, 2024, 2: 67-83.

[61]张汉五,万显荣,谢德强,等.外辐射源雷达距离徙动校正并行实现与实验研究[J].太赫兹科学与电子信息学报,2021,19(05):851-857.

[62]段化军,朱岱寅.带距离徙动校正的压缩感知PFA成像方法[J].中国图象图形学报,2014,19(07):1085-1094.

[63]邵文浩,朱莉,刘婕,等.基于截断sinc插值的毫米波全息成像算法优化[J].太赫兹科学与电子信息学报,2020,18(06):1035-1039.