Li Wenjie, Luan Yuchen, Chai Zicheng, Chen Longyong
National Key Laboratory of Microwave Imaging, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Rep. 2024 Sep 5;14(1):20759. doi: 10.1038/s41598-024-71620-y.
With the development of intelligent transportation systems, traffic supervision radar with wide coverage plays a crucial role in traffic management and vehicle-road coordination. The correlation between Doppler frequency and azimuth has been widely validated in wide coverage traffic supervision radar for high-precision velocity measurement. However, angular glint and noise of the nearby targets lead to a decrease in correlation between the azimuth and Doppler frequency, which negatively impacts the accuracy of velocity estimation. Currently, adopting separate filtering strategies for target azimuth and Doppler frequency has limited performance in enhancing correlation. This paper presents a joint observation model for azimuth and Doppler frequency to achieve the extraction of interrelated components from subspaces, which improves the accuracy of velocity measurement. The effectiveness of this approach is validated using data obtained from X-band and Ku-band sensors.
随着智能交通系统的发展，具有广泛覆盖范围的交通监测雷达在交通管理和车路协同中发挥着至关重要的作用。在具有广泛覆盖范围的交通监测雷达中，多普勒频率与方位角之间的相关性已被广泛验证，用于高精度速度测量。然而，附近目标的角闪烁和噪声会导致方位角与多普勒频率之间的相关性降低，这对速度估计的准确性产生负面影响。目前，对目标方位角和多普勒频率采用单独的滤波策略在增强相关性方面性能有限。本文提出了一种方位角和多普勒频率的联合观测模型，以实现从子空间中提取相关分量，从而提高速度测量的准确性。使用从X波段和Ku波段传感器获得的数据验证了该方法的有效性。
