作为自动驾驶系统的核心传感器之一，毫米波雷达在复杂环境下表现出了较强的抗干扰能力，然而其角度分辨率受实际天线孔径的限制，难以满足驾驶环境中对空间分辨率的要求。合成孔径雷达（Synthetic Aperture Radar，SAR）利用平台运动合成虚拟的大孔径天线，可以实现场景的两维高分辨成像，弥补了这一缺点。视频SAR成像技术在传统SAR成像的基础之上实现了高帧率成像，更适用于驾驶场景中环境感知系统的高实时性要求。因此，本文针对77GHz毫米波雷达视频SAR成像方法展开研究，提出了一种基于子孔径频谱融合的车载视频SAR实现方法，同时考虑到了非理想运动下的运动补偿问题。研究成果对车载SAR的实际应用具有重要的工程价值，主要研究内容如下：

(1)针对目前视频SAR成像方法计算复杂度高的问题，本文提出了一种基于子孔径相干拼接的车载视频SAR实现方法。该方法主要包括两个步骤：子孔径聚焦和拼接。在对子孔径数据进行聚焦处理阶段，使用改进的距离多普勒算法消除了车载SAR场景的空变问题，从而完成了对子孔径数据的聚焦处理。然后，设计了一种子孔径相干拼接策略来获得高分辨率的帧图像。其中，划分子孔径的目的是为了解决数据重叠模式下视频SAR数据重复处理的问题，同时选用高效的频域算法，为硬件模块减轻了处理负担。与现有的视频SAR实现方法相比，所提出的方法效率更高、性能更好。

(2)考虑到车载SAR平台容易道路状况和车辆自身控制问题的影响，难以保持匀速直线行驶，这种非理想运动轨迹将极大地影响成像质量。针对这一问题，本文设计了一种基于惯性导航系统（Inertial Navigation System, INS）的运动补偿方法。首先分析了INS测量误差的合理性，然后建立了车载SAR误差模型，将运动误差分解为沿运动方向误差和垂直于运动方向误差，并讨论了垂直于运动方向误差的空变性。随后对回波数据进行了一致相位补偿和空变相位补偿，从而消除了运动误差对成像结果的影响。最后，搭建了一套车载SAR外场实验平台，通过车载SAR实测数据，对所提方法进行了有效验证。

As one of the core sensors in the autonomous driving system, millimeter-wave radar has demonstrated the stronger anti-interference capability in complex environments. However, the millimeter-wave radar shows a lower spatial resolution in driving environments due to its lower angular resolution limited by the aperture of the antenna. Synthetic Aperture Radar (SAR) uses platform motion to synthesize a virtual large aperture antenna, enabling two-dimensional high-resolution imaging of scenes. Video SAR imaging technology achieves high frame-rate imaging, which meets the higher real-time demands of environment perception systems in driving scenes. Therefore, this paper carries out a study on the 77GHz millimeter-wave radar video SAR imaging method, proposing a automotive video SAR implementation method based on the sub-aperture spectral fusion while considering the motion compensation under non-ideal motion conditions. The research results have an important engineering value to the practical application of the vehicle-mounted SAR. The main research contents are as follows:

(1)This paper proposes a automotive video SAR implementation method based on sub-aperture coherent stitching to lower the high computational complexity in current video SAR imaging methods. The method consists of two main steps: sub-aperture focusing and stitching. First, the improved Range-Doppler algorithm can be used to eliminate the space-variant problem in automotive SAR scenes, further achieving the sub-aperture focusing processing. Then, a sub-aperture coherent stitching strategy is designed to obtain high-resolution frame images. The division of sub-apertures is aimed to lessen the duplicated processing of video SAR data under data overlap mode. Additionally, an efficient frequency domain algorithm is adopted to reduce the processing burden on hardware modules. Compared with existed video SAR implementation methods, the proposed method is better in efficiency and performance.

(2)Due to the impact of road conditions and vehicle self-control issues on the SAR platform, which makes it difficult to maintain constant speed and straight-line movement. The non-ideal motion trajectory will exert a profound impact on the imaging quality. To address this issue, this paper proposes a motion compensation method based on an Inertial Navigation System (INS). Firstly, the validity of INS measurement errors is analyzed, and then a SAR error model is established, which decomposes the motion error into errors along the motion direction and perpendicular to the motion direction, and then the spatial variability of errors perpendicular to the motion direction is discussed. Subsequently, the coherent phase compensation and the spatially variant phase compensation are performed on the echo data to eliminate the influence of motion errors on the imaging results. Finally, a automotive SAR field experiment platform is set up, and the proposed method is effectively validated through SAR real data obtained in the field experiment.

[1] Gao C, Wang G, Shi W, et al. Autonomous driving security: state of the art and challenges[J]. IEEE Internet of Things Journal, 2022, 9(10): 7572–7595.

[2] 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.

[3] 周林. 基于毫米波雷达和摄像头多源信息融合的环境感知研究[D]. 重庆: 重庆大学，2020.

[4] 刘翰. 77GHz车载毫米波雷达天线设计与性能优化[D]. 大连: 大连理工大学,2021.

[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] Tagliaferri D, Rizzi M, Nicoli M, et al. Navigation-Aided Automotive SAR for High-Resolution Imaging of Driving Environments[J]. IEEE Access, 2021, 9: 35599-35615.

[7] 郑昱, 李磊. 高性能机载实时SAR成像系统设计[J]. 现代雷达, 2015, 37(07): 20-21+25.

[8] 孟星伟, 董兰, 朱岱寅. 大斜视机载SAR多核DSP实时成像处理架构[J]. 现代雷达, 2021, 43(12): 7-14.

[9] 胡睿智. 视频合成孔径雷达成像理论与关键技术研究[D]. 成都: 电子科技大学, 2018.

[10] Iqbal A, Löffler M, Mejdoub, et al. Realistic SAR Implementation for Automotive Applications[C]//2020 17th European Radar Conference (EuRAD), Utrecht, Netherlands, 2021: 306-309.

[11] 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), Dresden, Germany, 2021: 1-4.

[12] 北京航空航天大学. 一种基于车载滑轨SAR成像的机场跑道异物检测方法及系统[P]. 中国专利: CN202110712228.3. 2021-09-28.

[13] Laribi A, Hahn M, Dickmann J, et al. Performance Investigation of Automotive SAR Imaging[C]//2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) IEEE, Munich, Germany 2018: 1-4.

[14] Farhadi M, Feger R, Fink J, et al. Adaption of Fast Factorized Back-Projection to Automotive SAR Applications[C]//2019 16th European Radar Conference (EuRAD) IEEE, Paris, France, 2019: 261-264.

[15] Wu H, Li X, Zwick T. Motion Compensation for Landmine Detecting Vehicle-borne SAR[C]//11-th International Radar Symposium, Vilnius, Lithuania, 2010: 1-4.

[16] Oshima A, Yamada H, Muramatsu S. Experimental Study on Automotive Millimeter Wave SAR in Curved Tracks[C]//2019 International Symposium on Antennas and Propagation (ISAP), Xi'an, China, 2019: 1-2.

[17] 吴晓彪, 孙合敏, 吴卫华等. 一种基于车载SAR的FOD检测系统及成像性能分析[J]. 空军预警学院学报, 2018, 32(4): 263-266.

[18] Tang K, Guo X, Liang X, et al. Implementation of Real-time Automotive SAR Imaging[C]//2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 2020: 1-4.

[19] 蒋留兵, 汪林, 车俐. 获取俯仰信息的车载合成孔径雷达成像方法[J]. 科学技术与工程, 2021, 21(15): 6337-6344.

[20] 唐世阳, 贺子轩, 蒋丞浩, 张林让, 张娟. 车载合成孔径雷达成像方法以及相关装置[P]. 陕西省：CN115453472A,2022-12-09.

[21] https://www.sandia.gov/radar/video/index.html.

[22] Wallace H. B. Video synthetic aperture radar (ViSAR)[R]. DARPA, Arlington, 2012.

[23] Kim S, Fan R, Dominski F. ViSAR: A 235 GHz radar for airborne applications[C]// 2018 IEEE Radar Conference (RadarConf18), 2018: 1549-1554.

[24] Gu C, Chang W, Li X, et al. The Formation of High-resolution FMCW SAR Video[C]//Progress in Electromagnetic Research Symposium (PIERS), 2016 IEEE, Shanghai, China, 2016: 496-499.

[25] Zhang B, Pi Y, Li J. Terahertz imaging radar with inverse aperture synthesis techniques: system structure, signal processing, and experiment results[J]. IEEE Sensors Journal, 2015, 15(1): 290-299.

[26] Zuo F, Li J. A persistent imaging method for video-SAR in terahertz band [C]//International Applied Computational Electromagnetics Society Symposium (ACES), 2017 IEEE, Suzhou, China, 2017: 1-2.

[27] Linnehan R, Miller J, Asadi A. Map-drift autofocus and scene stabilization for video-SAR[C]//2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, 2018: 1401-1405.

[28] Miller J, Bishop E, Doerry A. An application of backprojection for video SAR image formation exploiting a subaperature circular shift register[C]// Algorithms for Synthetic Aperture Radar Imagery, 2013. International Society for Optics and Photonics.

[29] Hawley R, Garber W. Aperture weighting technique for video synthetic aperture radar[C]//Algorithms for Synthetic Aperture Radar Imagery, 2011. International Society for Optics and Photonics.

[30] Damini A, Mantle V, Davidson G. A new approach to coherent change detection in VideoSAR imagery using stack averaged coherence[C]//2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, Canada, 2013:1-5.

[31] Hu R, Min R, Pi Y. Interpolation-free algorithm for persistent multi-frame imaging of video-SAR[J]. IET Radar, Sonar & Navigation, 2017, 11(6): 978.

[32] 赵雨露, 张群英, 李超, et al. 视频合成孔径雷达振动误差分析及补偿方案研究[J]. 雷达学报, 2015, 4(2): 230-239.

[33] 宋晓燊, 禹卫东. 条带式VideoSAR参数依赖关系的推导及应用[J].中国科学院大学学报, 2016, (01): 121-127.

[34] 孙伟, 孙进平, 张远, et al. 大斜视直升机载太赫兹ViSAR振动补偿成像算法[J].北京航空航天大学学报, 2016, (12): 2755-2761.

[35] 梁健, 张润宁, 包敏凤. 天基视频SAR系统设计及成像算法研究[J].中国空间科学技术, 2016, (06): 22-28.

[36] 梁健, 张润宁, 李晓云等. 基于子孔径ECS算法的天基视频SAR成像方法[J]. 中国空间科学技术, 2018, (06): 21-27.

[37] 涂标. 视频SAR运动补偿算法研究及成像软件实现[D]. 成都: 电子科技大学, 2021.

[38] Miller J, Bishop E, Doerry A. An application of backprojection for video SAR image formation exploiting a subaperature circular shift register[C]//SPIE 2013, 8746, 874609.

[39] Bishop E, Linnehan R, Doerry A. Video-SAR using higher order Taylor terms for differential range[C]//2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA, 2–6 May 2016: 1–4.

[40] Zuo F, Min R, Pi Y, et al. Improved Method of Video Synthetic Aperture Radar Imaging Algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6): 897–901.

[41] Gao A, Sun B, Li J, et al. A Parameter-Adjusting Autoregistration Imaging Algorithm for Video Synthetic Aperture Radar. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5215414.

[42] 王颖, 曲长文, 苏峰, 周强. FMCW SAR距离徙动成像算法研究[J]. 中国电子科学研究院学报, 2008(05): 478-482.

[43] 保铮, 刑孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2014: 1-18．

[44] 蔡永俊. 调频连续波合成孔径雷达成像研究与系统实现[D]. 合肥: 中国科学院国家空间科学中心, 2016.

[45] 蔡永俊, 张祥坤, 姜景山. 调频连续波合成孔径雷达回波建模与信号分析[J]. 电波科学学报, 2015, 30(6): 1157-1163.

[46] Kang Y, Jung D, Park S. Validity of Stop-and-Go Approximation in High-Resolution Ku-band FMCW SAR with High-Velocity Platform[C]//2021 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Bali, Indonesia, 2021:1-4

[47] Yan H, Mao X, Zhang J, et al. Frame rate analysis of video synthetic aperture radar (ViSAR)[C]//2016 International Symposium on Antennas and Propagation (ISAP), Okinawa, Japan, 24–28 October 2016: 446–447.

[48] Li X, Liu G, Ni J. Autofocusing of ISAR images based on entropy minimization. IEEE Transactions on Aerospace and Electronic Systems. 1999, 35(4): 1240–1252.