摘要
提出了一种基于滑动窗能量检测的改进后向投影算法以抑制经典后向投影算法所产生的伪影。该算法首先对B-scan数据进行滑动窗能量检测,以获知探测场景中目标水平方向位置、个数以及相对能量强弱。然后在根据所获目标个数划分的不同区域中,选择目标位置所对应的A-scan数据作为参考数据来计算皮尔逊加权系数。此外,根据目标的相对能量强弱,对弱目标加权因子进行补偿。最终,通过加权互相关处理来对成像区域中各点的散射响应进行重构。将该算法应用于格雷互补码探地雷达中,实验结果证明:所提算法能在不削弱目标能量的同时,显著抑制成像结果中的伪影。
An improved back projection algorithm based on sliding window energy detection is proposed,which can suppress the artifacts generated by the classic back projection algorithm.By utilizing the sliding window energy detection first,the horizontal position and number as well as the relative energies of targets can be obtained from the B-scan data.Then,in each region divided according to the number of targets obtained,the A-scan data of the target position are selected as the reference data to calculate the Pearson weighting coefficient.The weighting coefficients of the weak targets are further compensated according to the relative energy of targets.Finally,the scattering response of each point in the imaging region is reconstructed by weighting and cross correlation.The algorithm is applied to Golay complementary code ground penetrating radar.The experimental results prove that the proposed algorithm can significantly suppress the artifacts in the imaging results without weakening the target energy.
作者
杨军
李静霞
刘丽
徐航
王冰洁
YANG Jun;LI Jingxia;LIU Li;XU Hang;WANG Bingjie(Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province,Taiyuan University of Technology,Taiyuan Shanxi 030024,China;College of Physics and Optoelectronics,Taiyuan University of Technology,Taiyuan Shanxi 030024,China)
出处
《电子器件》
CAS
北大核心
2022年第6期1441-1447,共7页
Chinese Journal of Electron Devices
基金
国家自然科学基金项目(41604127,42174175)
山西省自然科学基金项目(201801D221185)
山西省重点研发计划项目(社会发展领域)(201803D31037,201803D121057)。
关键词
滑动窗能量检测
后向投影
探地雷达
抑制伪影
sliding window energy detection
back projection
ground penetrating radar
artifact suppression
作者简介
杨军(1996-),男,硕士研究生,主要研究方向为探地雷达成像算法研究,yangjun0812@link.tyut.edu.cn;李静霞(1983-),女,副教授,博士,主要研究方向为混沌信号产生、新型探地雷达系统,lijingxia@tyut.edu.cn。
