毫米波雷达在智慧交通领域得到广泛的应用，随着城市交通状况的日益复杂，对雷达检测精度提出更高要求。由于交通场景中目标过多、目标速度快引起雷达在探测精度上无法满足需求。因此，开展多目标参数估计方法研究对提高雷达探测精度具有十分重要的意义。

针对TDM-MIMO雷达多目标检测过程中因速度模糊导致速度估计精度降低的问题，提出一种基于重叠阵元MIMO阵列的速度解模糊方法。基于虚拟孔径原理在传统MIMO天线阵列中引入重叠阵元，构建重叠阵元MIMO天线阵列。通过对虚拟通道进行二维快速傅里叶变换和恒虚警检测获得目标距离和模糊速度。利用重叠阵元回波信号的相位差值进行频率估计，结合频率估计结果与速度模糊模型提取速度模糊数，利用模糊数和模糊速度估计目标实际速度。针对解模糊过程中“速度跳变”问题，通过引入频谱搬移方法对速度区间进行转换，消除速度区间边界的速度跳变值，实现多目标的距离和速度估计。通过蒙特卡洛仿真实验，信噪比为15 dB的条件下，解模糊正确率为100%，速度误差为0.1 m/s。仿真结果表明，该方法有效解决速度模糊问题，提高速度估计精度。

针对TDM-MIMO雷达多目标检测过程中因多普勒-角度耦合导致角度估计精度降低的问题，提出一种基于重叠阵元运动补偿的角度估计方法。该方法利用重叠阵元间的空间位置特性构建相位补偿因子，并对天线阵列相位进行补偿，消除目标运动引入的相位误差。通过在角度快速傅里叶变换算法中引入循环迭代的方法估计阵列位置参数，消除阵元位置误差导致的相位差，提高角度估计精度。通过蒙特卡洛仿真实验，信噪比为15 dB的条件下，角度误差为0.1°。仿真结果表明，该方法有效解决多普勒-角度耦合问题，提高角度估计精度。

结合实际项目需求，对毫米波雷达的天线阵列结构、帧结构和系统参数进行分析与设计。基于CAL60S244射频芯片和Xilinx ZYNQ-7020信号处理芯片的毫米波雷达硬件平台对优化阵列和改进算法进行实现。基于城市交通场景采集的车辆数据集进行测试，实验结果表明，该方法能够有效解决多普勒-角度耦合和速度模糊问题，实现对运动目标的距离、速度和角度精确估计，验证了优化阵列和改进算法的可行性，满足交通雷达对车辆监测实时性和准确性的需求。

Traffic radar monitoring is the basis of smart transportation. Millimeter-wave radar has the advantages of all-day, all-weather, and low cost, so it has been widely used in the field of smart transportation. With the increasing complexity of urban traffic conditions, higher requirements are placed on detection accuracy. Compared with traditional radars, time-division multiplexing multiple-input multiple-output (TDM-MIMO) radar has higher detection accuracy and resolution, which is more in line with practical application requirements. Due to the excessive number of targets in the traffic scene and the fast target velocity, the radar cannot meet the requirements in terms of detection accuracy. Therefore, it is of great significance to carry out research on multi-target parameter estimation methods to improve the detection accuracy of radar.

In order to solve the problem of low velocity estimation accuracy due to velocity ambiguity in the process of multi-target detection in TDM-MIMO radar, a velocity disambiguation method based on overlapping MIMO arrays is proposed. Based on the principle of virtual aperture, overlapping elements are introduced into the traditional MIMO antenna array to construct an overlapping element MIMO antenna array. The target distance and ambiguity velocity are obtained by performing two-dimensional fast Fourier transform and constant false alarm detection on the virtual channel. The frequency is estimated by using the phase difference value of the echo signals of overlapping array elements, and the velocity ambiguity number is extracted by combining the frequency estimation result and the velocity ambiguity model, and the actual velocity of the target is estimated by combining the velocity ambiguity number and the ambiguity velocity. In order to solve the "velocity jump" problem in the process of disambiguation, the velocity range is converted by introducing the spectrum shift method, and the velocity jump value at the boundary of the velocity range is eliminated, so as to realize the distance and velocity estimation of multiple targets. Through Monte Carlo simulation experiments, under the condition of signal-to-noise ratio of 15 dB, the correct rate of disambiguation is 100%, and the velocity error is 0.1 m/s. The simulation results show that the method can effectively solve the velocity ambiguity and improve the velocity estimation accuracy.

In order to solve the problem of low angle estimation accuracy due to Doppler-angle coupling in the process of multi-target detection in TDM-MIMO radar, a motion compensation method based on overlapping element MIMO array is proposed. The method uses the spatial position characteristics of overlapping array elements to construct phase compensation factors, and compensates the phase of the antenna array to eliminate the phase error caused by target motion. By introducing the cyclic iteration method into the angle fast Fourier transform algorithm to estimate the array position parameters, the phase difference caused by the position error of the array elements is eliminated, and the angle estimation accuracy is improved. Through Monte Carlo simulation experiments, the angle error is 0.1° when the signal-to-noise ratio is 15 dB. The simulation results show that the method can effectively solve the Doppler-angle coupling and improve the angle estimation accuracy.

The millimeter wave radar hardware platform based on CAL60S244 RF chip and Xilinx ZYNQ-7020 signal processing chip implements the optimized array and improved algorithm. Combined with the actual project requirements, the radar's antenna array structure, frame structure and system parameters are analyzed and designed. Based on the vehicle dataset collected from urban traffic scenes, the experimental results show that the method can effectively solve the Doppler-angle coupling and velocity ambiguity problems, realize the accurate estimation of the distance, velocity and angle of the moving target, and meet the real-time and accuracy requirements of traffic radar for vehicle monitoring.

[1]赵娜,袁家斌,徐晗.智能交通系统综述[J].计算机科学,2014,41(11):7-11.

[2]Pribyl O, Pribyl P, Lom M, et al. Modeling of Smart Cities Based on ITS Architecture[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11(4):28-36.

[3]田为广,徐海黎,殷戎飞,等.基于地磁传感器的车流量智能检测系统设计[J].传感技术学报,2021,34(1):137-142.

[4]王翔,肖建力.基于视频的交通参数检测综述[J].上海理工大学学报,2016,37(5):479-486.

[5]赵永波,刘宏伟.MIMO雷达技术综述[J].数据采集与处理,2018,33(3):389-399.

[6]Sun S, Petropulu A P, Poor H V. MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges[J]. IEEE Signal Processing Magazine, 2020, 37(4):98-117.

[7]Roos F, Bechter J, Knill C, et al. Radar Sensors for Autonomous Driving: Modulation Schemes and Interference Mitigation[J]. IEEE Microwave Magazine, 2019, 20(9):58-72.

[8]Waldschmidt C, Hasch J, Menzel W. Automotive Radar — From First Efforts to Future Systems[J]. IEEE Journal of Microwaves, 2021, 1(1):135-148.

[9]Engels F, Heidenreich P, Zoubir A M, et al. Advances in Automotive Radar: A framework on computationally efficient high-resolution frequency estimation[J]. IEEE Signal Processing Magazine, 2017, 34(2):36-46.

[10]Hakobyan G, Yang B. High-Performance Automotive Radar: A Review of Signal Processing Algorithms and Modulation Schemes[J]. IEEE Signal Processing Magazine, 2019, 36(5):32-44.

[11]Wen J, Yulin H, Jianyu Y. Automatic Censoring CFAR Detector Based on Ordered Data Difference for Low-Flying Helicopter Safety[J]. Sensors, 2016, 16(7):1055.

[12]Kan Y, Zhu Y, Tang L, et al. FGG-NUFFT-Based Method for Near-Field 3-D Imaging Using Millimeter Waves[J]. Sensors, 2016, 16(9):1525.

[13]Felguera-Martin D, Gonzalez-Partida J T, Almorox-Gonzalez P, et al. Vehicular Traffic Surveillance and Road Lane Detection Using Radar Interferometry[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3):959-970.

[14]Lopez A A, AD De Quevedo, Yuste F S, et al. Coherent Signal Processing for Traffic Flow Measuring Radar Sensor[J]. IEEE Sensors Journal, 2018, 18(12):4803-4813.

[15]Ho T J, Chung M J. An approach to traffic flow detection improvements of non-contact microwave radar detectors[C]// 2016 International Conference on Applied System Innovation (ICASI). IEEE, 2016:1-4.

[16]张浩,薛伟,余稳,等.一种应用于毫米波车流量检测雷达的背景功率谱识别方法[J].红外与毫米波学报,2008,27(6):437-441.

[17]Miyamoto K, Nakagawa Y. 79GHz-band millimeter-wave radar high precision and wide field of view technologies[C]// 2014 Asia-Pacific Microwave Conference. IEEE, 2014:546-548.

[18]Ho T J, Chung M J. Information-Aided Smart Schemes for Vehicle Flow Detection Enhancements of Traffic Microwave Radar Detectors[J]. Applied Sciences, 2016, 6(7):196.

[19]Lien J, Gillian N, Karagozler M E, et al. Soli: Ubiquitous Gesture Sensing with Millimeter Wave Radar[J]. ACM Transactions on Graphics, 2016, 35(4):142.1-142.19.

[20]李巍,齐巍,丁赤飚,等.基于分布式雷达的宽带脉冲三维测距机制及方法研究[J].电子与信息学报,2015,37(3):643-650.

[21]张晨,史再峰,郭炜,等.调频连续波太赫兹雷达近程测距精度提高算法[J].传感器与微系统,2016,35(9):141-143.

[22]邹林,黄述康,钱璐,等.LFMCW车载雷达解速度模糊测角新方法[J].电子科技大学学报,2020,49(6):801-805.

[23]王元恺,肖泽龙,许建中,等.一种改进的FMCW雷达线性调频序列波形[J].电子学报,2017,45(6):1288-1293.

[24]周奇特,李朝晖.组合时延估计及人工时延方法用于脉冲相干多普勒测速去模糊[J].声学学报,2018,43(4):582-591.

[25]Xu L, Lien J, Li J. Doppler-Range Processing for Enhanced High-Speed Moving Target Detection Using LFMCW Automotive Radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(1):568-580.

[26]Scherhufl M, Hammer F, Pichler-Scheder M, et al. Radar Distance Measurement With Viterbi Algorithm to Resolve Phase Ambiguity[J]. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(9):3784-3793.

[27]Gonzalez H A, Liu C, Vogginger B, et al. Doppler disambiguation in MIMO FMCW radars with binary phase modulation[J]. IET Radar Sonar and Navigation, 2021, 15(8):884-901.

[28]Baral A B, Torlak M. Joint Doppler Frequency and Direction of Arrival Estimation for TDM MIMO Automotive Radars[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(4):980-995.

[29]Xiong X, Hui L, Deng Z, et al. Micro-Doppler Ambiguity Resolution With Variable Shrinkage Ratio Based on Time-Delayed Cross Correlation Processing for Wideband Radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4):1906-1917.

[30]Silva B, Fraidenraich G. Performance Analysis of the Classic and Robust Chinese Remainder Theorems in Pulsed Doppler Radars[J]. IEEE Transactions on Signal Processing, 2018, 66(18):4898-4903.

[31]Beltrao G, Pralon L, Barreto A, et al. Subpulse Processing for Unambiguous Doppler Estimation in Pulse Doppler Noise Radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(6):3813-3826.

[32]王超,王岩飞,王琦,等.基于回波序列最小二乘拟合的高分辨率SAR运动目标速度估计[J].电子与信息学报,2019,41(5):1055-1062.

[33]Zhang W, He N, He Z, et al. Approach of 2D direction of arrival estimation of FMCW traffic radar by utilising 1D array[J]. Electronics Letters, 2020, 56(2):97-98.

[34]Hyungsoo N, Ying-Chun L, Byunggil C, et al. 3D-Subspace-Based Auto-Paired Azimuth Angle, Elevation Angle, and Range Estimation for 24G FMCW Radar with an L-Shaped Array[J]. Sensors, 2018, 18(4):1113.

[35]陈宝欣,关键,董云龙,等.多频连续波雷达与角度-距离联合估计方法[J].电子学报,2020,48(02):375-383.

[36]陈桢.基于空间域压缩感知的车载雷达目标定位算法[J].汽车安全与节能学报,2019,10(02):192-199.

[37]王军,闫锋刚,马文洁,等.基于Laplace先验的Bayes压缩感知波达方向估计[J].电子与信息学报,2015,37(4):817-823.

[38]Hu X, Lu M, Li Y, et al. Motion compensation for TDM MIMO radar by sparse reconstruction[J]. Electronics Letters, 2017, 53(24):1604-1606.

[39]Bechter J, Roos F, Waldschmidt C. Compensation of Motion-Induced Phase Errors in TDM MIMO Radars[J]. IEEE Microwave and Wireless Components Letters, 2017, 27(12):1164-1166.

[40]Lin Y, Sun Z, Guo M, et al. Phase Compensation Method Based on Reference-Element for SAA FMCW Radar[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(21):2097-2101.

[41]Hu X, Li Y, Lu M, et al. A Multi-Carrier-Frequency Random-Transmission Chirp Sequence for TDM MIMO Automotive Radar[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4):3672-3685.

[42]Haefner S, Thoma R. Compensation of Motion-Induced Phase Errors and Enhancement of Doppler Unambiguity in TDM-MIMO Systems by Model-Based Estimation[J]. IEEE Sensors Letters, 2020, 4(10):1-4.

[43]Neemat S, Krasnov O, Zwan F, et al. Decoupling the Doppler Ambiguity Interval from the Maximum Operational Range and Range-Resolution in FMCW Radars[J]. IEEE Sensors Journal, 2020, 20(11):5992-6003.

[44]Jung J, Lim S, Kim S C, et al. Solving Doppler-Angle Ambiguity of BPSK-MIMO FMCW Radar System[J]. IEEE Access,2021, 9:120347-120357

[45]Nguyen M Q, Feger R, Bechter J, et al. Fast-Chirp FDMA MIMO Radar System Using Range-Division Multiple-Access and Doppler-Division Multiple-Access[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(1):1136-1148.

[46]Li H, Li S, Li Z, et al. Compressed Sensing Imaging with Compensation of Motion Errors for MIMO Radar[J]. Remote Sensing, 2021, 13(23):4909.

[47]Zhang R, Zhang Z, Zhang X, et al. Phase compensation transform for human detection with LFMCW radar[J]. Signal processing, 2020, 172:107565.1-107565.12.

[48]Casademont T, Hort M, Scharff L, et al. I-Channel FMCW Doppler Radar for Long-Range and High-Velocity Targets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.

[49]Lim H S, Park H M, Lee J E, et al. Lane-by-Lane Traffic Monitoring Using 24.1 GHz FMCW Radar System[J]. IEEE Access, 2021, 9:14677-14687.

[50]杨守国,李勇,张昆辉,等.基于降维的双基地MIMO雷达收发阵列互耦和幅相误差校正算法[J].系统工程与电子技术,2018,40(12):2668-2674.

[51]Zhang W, Wang P, He N, et al. Super Resolution DOA Based on Relative Motion for FMCW Automotive Radar[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8):8698-8709.

[52]Xu S, Yarovoy A. Joint Features Extraction for Multiple Moving Targets Using (Ultra-)Wideband FMCW Signals in the Presence of Doppler Ambiguity[J]. IEEE Transactions on Signal Processing, 2020, 68:6562-6577.