基于主元分析法的浅地层小目标探测算法

AN ALGORITHM FOR DETECTION OF SHALLOWLY BURIED SMALL OBJECTS BASED ON PRINCIPAL COMPONENT ANALYSIS

在应用便携式探地雷达(GPR)进行浅地层中的小目标探测时,由于目标回波能量小,目标回波与直达波叠加,加上地表杂波及探测产生的杂波,大大降低了目标回波的信噪比,使在复杂的浅地层介质之中,难以对小目标进行准确探测。因此提出了基于主元分析(PCA)法的浅地层小目标探测算法,通过PCA分解,将当前雷达扫描数据映射到背景所在的投影方向上,建立检验函数并和自适应阈值比较,判断是否含有目标回波信息,结合动态背景更新即可实现浅地层中小目标的探测。用PCA法对在沙土、红土、黏土、草地试验的数据进行处理,结果表明PCA法能够探测出浅地层中的小目标。

It is difficult to exactly detect small objects in complex shallow subsurface.As the energy of backscattered signal from the target is low,backscattered signals from target and ground surface are overlapped.In addition,clutters created by ground surface and movement strongly depress the signal-to-clutter ratio when shallowly buried small objects are detected by portable ground penetrating radar（GPR）.An algorithm for detection of shallowly buried objects based on principal component analysis（PCA） is thus proposed in this paper.Via PCA decomposition,current A-scan data are projected onto the projecting direction of background data.A set test function is compared with adaptive threshold to decide if current A-scan data are from an object.Detection of shallowly buried small objects can be achieved in combination with background data dynamic updating.The data tested in sand,laterite,clay and lawn were processed,and the results show that shallowly buried objects can be detected using algorithm based on PCA.