存在幅相误差时二维稳健超分辨测角算法  

Algorithm for estimation of the two-dimensional robust super-resolution angle under amplitude and phases uncertainty background

作　　者：刘敏提 曾操[1] 胡树林 陈建忠[1] 李军[1] 李世东[1] 廖桂生[1] LIU Minti;ZENG Cao;HU Shulin;CHENG Jianzhong;LI Jun;LI Shidong;LIAO Guisheng(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学雷达信号处理全国重点实验室,陕西西安710071

出　　处：《西安电子科技大学学报》2024年第3期55-62,共8页Journal of Xidian University

基　　金：国家自然科学基金(61621005,61771015)。

摘　　要：针对4D车载毫米波雷达在俯仰与方位维角度分辨力较低、阵列存在幅相误差时测角有偏的问题,提出一种基于快速稀疏贝叶斯学习的稳健二维超分辨测角方法。首先,利用空域稀疏性特点,对角度域空间进行栅格划分,构建了存在幅相误差时的二维超分辨测角信号模型;然后,通过固定点更新的MacKay SBL重构算法实现了多个邻近目标二维角度估计,并利用基于向量点乘的自校正算法对相位误差进行估计,以对有偏的角度估计进行修正;最后,给出了多输入多输出虚拟阵列下的二维角度估计的克拉美-罗界,并分析了所提算法的计算复杂度。仿真结果表明,在大陆ARS548雷达实际12发16收天线布局下,通过对比6种超分辨测角算法,所提方法在低信噪比、少量快拍下和幅相误差较小时,具有较高的角度分辨力与较低的均方根误差。In order to address the issues of low angle resolution in elevation and azimuth dimensions of the 4D vehicle-mounted millimeter wave radar,as well as the biased angle measurement when the array includes amplitude and phase defects.A robust two-dimensional super-resolution angle estimation method based on fast sparse Bayesian Learning(FSBL)is suggested as a solution to this issue.First,a two-dimensional super-resolution angle signal model with amplitude and phase errors is built by using grids to split the angle domain space depending on spatial sparsity.Then,the two-dimensional angle estimation for spatial proximity targets is obtained using the fixed-point updated based MacKay SBL reconstruction algorithm,with the phase error and biased angle compensation calibrated using the self-correcting algorithm based on vector dot product.Finally,the computational complexity of the proposed algorithm is analyzed,and the Cramer-Rao Lower Bound(CRB)for two-dimensional angle estimation under MIMO non-uniform sparse arrays is provided.By comparing six distinct categories of super-resolution algorithms,simulation results demonstrate that the proposed method has a high angle resolution and a low root mean square error(RMSE)in a low SNR and few snapshot numbers under the actual layout of 12 transmitting and 16 receiving antennas for the continental ARS548 radar.

关 键 词：超分辨 多输入多输出阵列 毫米波雷达 贝叶斯学习 幅相误差 

分 类 号：TN911.7[电子电信—通信与信息系统]