摘要
针对高分辨率穿墙雷达系统采用宽带信号和大孔径阵列天线而产生大量数据的问题,在雷达成像过程中采用压缩感知理论以降低数据量需求,提出一种分式平滑l_(0)范数的穿墙雷达稀疏成像算法。通过对墙体存在所导致的电磁波传播时延和折射进行补偿与修正,重新构建了穿墙雷达成像模型,并利用墙后目标稀疏性的特点,将成像问题转化为l_(0)范数最小化问题。重构图像的过程中提出了一种新的平滑函数以提高对l_(0)范数的逼近程度,并用最速下降法求解最优化问题。仿真结果表明,所提算法能够在欠采样条件下实现对目标位置的精确成像,具有较优的抗噪性能。
To address the problem of high-resolution through-the-wall radar systems using wideband signals and large array apertures that generate large amounts of data,a sparse imaging algorithm for throughthe-wall radar based on a fractional smoothed l_(0)norm is proposed using compressed sensing theory in the through-the-wall radar imaging process to reduce data volume requirements.By compensating and correcting for the electromagnetic wave propagation time delay and refraction caused by the presence of a wall,the imaging model for through-wall radar is reconstructed and the imaging problem is transformed into a l_(0)norm minimization problem by exploiting the sparsity of the target behind the wall.A new smoothing function is applied to the reconstructed image to improve the approximation to the l_(0)norm,and the optimization problem is solved using the fastest descent method.The simulation results show that the proposed algorithm is able to achieve accurate imaging of the target position under low data volume and has better noise immunity.
作者
李家强
杨广乐
徐必勇
胡张燕
陈金立
LI Jia-qiang;YANG Guang-le;XU Bi-yong;HU Zhang-yan;CHEN Jin-li(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《中国电子科学研究院学报》
北大核心
2022年第12期1147-1153,共7页
Journal of China Academy of Electronics and Information Technology
基金
江苏省自然科学基金(BK20191399)
国家自然科学基金(62071238)
关键词
穿墙雷达成像
压缩感知
l_(0)范数
稀疏信号重构
through-the-wall radar imaging
compressed sensing
l_(0)norm
sparse signal reconstruction
作者简介
李家强(1976—),副教授,主要研究方向为雷达信号处理;杨广乐(1998—),硕士研究生,主要研究方向为穿墙雷达成像技术;徐必勇(1977-),研究员,主要研究方向为电气自动化、机器人控制;胡张燕(1997—),硕士研究生,主要研究方向为SAR成像技术;陈金立(1982—),副教授,主要研究方向为MIMO雷达信号处理。