毫米波雷达凭借其全天时、全天候的工作特性，已成为自动驾驶系统的核心传感器之一。然而，受限于物理天线孔径，其角度分辨率难以满足复杂场景下的高精度成像需求。合成孔径雷达（Synthetic Aperture Radar，SAR）技术通过利用平台运动构建虚拟孔径，可突破这一限制，实现二维高分辨率成像。由于道路环境复杂，汽车运动轨迹机动灵活，导致传统SAR成像算法面临距离-方位耦合性严重、空变效应显著等挑战。因此，本文针对车载毫米波SAR成像的关键技术展开研究，提出了一种适应机动轨迹平台的高精度成像方法。研究成果对车载SAR技术的工程化应用具有重要意义，主要研究内容如下：

（1）针对车载平台机动轨迹引起的三维加速度效应导致的非均匀采样问题，提出了一种改进的极坐标格式算法（Polar Format Algorithm, PFA）。首先，基于惯性导航系统数据，采用多项式拟合方法精确估计平台速度矢量。其次，提出了一种等效距离模型，基于多普勒频率与瞬时斜距关系实现目标距离历程重构。最后，通过慢时间重采样预处理有效消除加速度的影响，并改进传统PFA算法中的二维插值映射函数，实现场景目标聚焦处理。仿真结果和性能评估验证了曲线运动轨迹情形下所提算法的有效性。

（2）考虑到汽车在实际行驶过程中会受到路面颠簸、车辆自身控制等多种因素的影响，难以保持理想的机动轨迹，因此存在运动误差。由于车载平台的近距成像特性，这些运动误差会影响距离历程，进而影响最终的成像质量。针对这一问题，本文基于建立的车载SAR运动误差模型，将误差分为运动分量误差和随机分量误差。针对运动误差，本文提出了一种改进的两步补偿方法进行消除。结合改进的PFA算法，最终实现对目标的聚焦成像。最后，通过仿真与实测数据验证了所提算法在车载平台机动轨迹下的有效性。

Millimeter wave radar has become one of the core sensors of automatic driving system by virtue of its all-day and all-weather working characteristics. However, due to the limitation of physical antenna aperture, the angular resolution is difficult to meet the high precision imaging requirements in complex scenes. Synthetic Aperture Radar (SAR) technology can overcome this limitation by constructing virtual aperture using platform motion to achieve two-dimensional high-resolution imaging. Due to the complex road environment and flexible vehicle trajectory, traditional SAR imaging algorithms are faced with serious range-azimuth coupling and significant space-variation effect. Therefore, in this paper, the key technologies of vehicle-mounted millimeter wave SAR imaging are studied, and a high-precision imaging method adapted to maneuvering trajectory platform is proposed. The research results are of great significance to the engineering application of vehicle-mounted SAR technology, and the main research contents are as follows:

(1)An improved Polar Format Algorithm (PFA) was proposed to solve the problem of non-uniform sampling caused by three-dimensional acceleration effect caused by maneuvering trajectory of vehicle platform. Firstly, the platform velocity vector is estimated by polynomial fitting method based on the inertial navigation system data. Secondly, an equivalent distance model is proposed to reconstruct the target distance history based on the relationship between Doppler frequency and instantaneous oblique distance. Finally, slow time resampling pre-processing is used to eliminate the effect of acceleration effectively, and the two-dimensional interpolation mapping function in the traditional PFA algorithm is improved to realize the scene target focusing processing. Simulation results and performance evaluation verify the effectiveness of the proposed algorithm in the case of curved motion trajectory.

(2)Considering that the car will be affected by many factors such as road bumps and the vehicle's own control in the actual driving process, it is difficult to maintain the ideal maneuvering trajectory, so there are motion errors. Due to the close-range imaging characteristics of the on-board platform, these motion errors will affect the range history and thus the final imaging quality. To solve this problem, based on the established motion error model of vehicular SAR, this paper divides the error into motion component error and random component error. In this paper, an improved two-step compensation method is proposed to eliminate the motion error. Combined with the improved PFA algorithm, the focused imaging of the target was finally realized. Finally, the effectiveness of the proposed algorithm is verified by simulation and measured data under the maneuvering trajectory of the vehicle platform.

[1]朱小波,谭兴文.自动驾驶汽车环境感知与传感器融合技术[J].汽车与新动力,2024,7(04):24-27.

[2]张叠,刘娇.自动驾驶感知技术综述[J].科技视界,2024,14(18):3-7.

[3]Wei Z, Zhang F, Chang S, Liu Y, Wu H, Feng Z. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review[J]. Sensors, 2022, 22, 2542.

[4]Srivastav A, Mandal S. Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges[J]. IEEE Access, vol. 11, pp. 97147-97168, 2023.

[5]金一苇.基于4D毫米波雷达与图像融合的自动驾驶3D目标检测方法研究[D].电子科技大学,2024.

[6]Hu Z, Su T, Xu D, et al. A practical approach for calibration of MMW MIMO near-field imaging[J]. Signal Processing,2024,225109634-109634.

[7]张远,肖宝华,杨大林.基于4D毫米波雷达点云的多目标跟踪算法[J].科技资讯, 2024,22(01): 38-42.

[8]Geng X ,Wang J ,Yang B , et al. Joint velocity and angle estimation for single-cycle TDM MIMO automotive radar[J]. Digital Signal Processing,2024,153104617-104617.

[9]Cumming I G, Wong F H. Digital Processing of Synthetic Aperture Radar Data Algorithms and Implementation[M]. Beijing: Publishing House of Electronics Industry, 2012.

[10]Franceschetti Giorgio, Lanari Riccardo. Synthetic Aperture Radar Processing[M]. CRC Press:2018.2.6.

[11]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.

[12]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.

[13]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.

[14]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.

[15]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.

[16]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.

[17]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.

[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]Oshima A, Yamada H and 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.

[20]R. Hu, B. S. M. R. Rao, A. Murtada, M. Alaee-Kerahroodi and B. Ottersten. Automotive Squint-Forward-Looking SAR: High Resolution and Early Warning[J]. IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 4, pp. 904-912, June 2021.

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

[22]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.

[23]张远,肖宝华,杨大林,等.车载毫米波SAR二维波数域成像算法[C]//中国电子学会数字信号处理专家委员会.第十四届全国DSP应用技术学术会议论文集.北方工业大学;,2022:5.

[24]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.

[25]袁孟北,晋良念.车载毫米波FMCW SAR扩展目标聚焦成像方法[J].雷达科学与技术,2023,21(06): 689-696.

[26]Jiang C, Tang S, Zhang L, Sun J. Real Data Imaging Approach Design for Automotive SAR Experiments[C]//2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China, 2021: 386-388.

[27]涂胜龙.高分辨车载FMCW SAR成像方法研究[D].西安电子科技大学,2022.

[28]李凌杰,杨军,黄顺吉.一种高效率SAR成像处理算法[J].电子科技大学学报,1996,(04):362-367.

[29]Liu Y, Wang J, Bao Y, Mao X. Automotive Millimeter Wave SAR Imaging in Curved Trajectory[C]//2022 14th International Conference on Signal Processing Systems (ICSPS), Jiangsu, China, 2022: 493-495.

[30]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.

[31]Guo P, Wu F, Tang S, Jiang C, Liu C. Implementation Method of Automotive Video SAR (ViSAR) Based on Sub-Aperture Spectrum Fusion[J]. Remote Sensing. 2023, 15, 476.

[32]Liu Y, Tao M, Shi T, Wang J, Wang J, Mao X. Sub-Aperture Polar Format Algorithm for Curved Trajectory Millimeter Wave Radar Imaging[J]. IEEE Transactions on Radar Systems. 2024, 2, pp. 67-83.

[33]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.

[34]Fornaro G. Trajectory deviations in airborne SAR: Analysis and compensation[J]. IEEE Trans. Aerosp. Electron. Syst., vol. 35, no. 3, pp. 9971009, Jul. 1999.

[35]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/OL].系统工程与电子技术,2024:1-12.

[36]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.

[37]FEGER R, HADERER A, and STELZER A. Experimental verification of a 77-GHz synthetic aperture radar system for automotive applications[C]//2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Nagoya, Japan, 2017: 111–114.

[38]Wu H, Zwirello L, Li X, et al. Motion compensation with one-axis gyroscope and two-axis accelerometer for automotive SAR[C]//2011 German Microwave Conference, 2011:1-4.

[39]STEINER M, GREBNER T, WALDSCHMIDT C. Millimeter-wave SAR-imaging with radar networks based on radar self-localization[J]. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(11): 4652–4661.

[40]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(3):85-94.

[41]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), Linz, Austria, 2020: 1–4.

[42]FARHADI M, FEGER R, FINK J, et al. Space-variant phase error estimation and correction for automotive SAR[C]//The 2020 17th European Radar Conference (EuRAD), Utrecht, Netherlands, 2021: 310–313.

[43]MANZONI M, RIZZI M, TEBALDINI S, et al. Residual motion compensation in automotive MIMO SAR imaging[C]//2022 IEEE Radar Conference (RadarConf22), New York City, USA, 2022: 1–7.

[44]MANZONI M, TAGLIAFERRI D, RIZZI M, et al. Motion estimation and compensation in automotive MIMO SAR[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 1756–1772.

[45]Merlo J, Nanzer J. Multi-Pass Automotive Synthetic Aperture Radar Image Fusion[C]//2023 IEEE Radar Conference (RadarConf23), San Antonio,TX, USA, 2023, pp.1-6.

[46]Han J, Tang S, Chen J, et al. Precise Motion Compensation Approach for High-Resolution Multirotor UAV SAR in the Presence of Multiple Errors[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2024, 17, 15148-15165.