基于瞬变电磁探测的未爆弹分类研究  

Classification of Unexploded Ordnance Based on Transient Electromagnetic Sensing

作　　者：邓昊原 张爽[1] 陈曙东[1] DENG Haoyuan;ZHANG Shuang;CHEN Shudong(College of Electronic and Science Engineering,Jilin University,Changchun 130012,China)

机构地区：[1]吉林大学电子科学与工程学院,长春130012

出　　处：《吉林大学学报（信息科学版）》2023年第2期265-271,共7页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(41704145)。

摘　　要：针对电磁法对有害的未爆弹和无害的金属弹片等都具有灵敏的响应,因此探测误报率较高,导致后续的清理工作极为耗时的问题,基于便携式与拖曳式两种瞬变电磁探测系统,对多个未爆弹与无害目标进行探测并估计地下目标的特征响应。根据估计的电磁特性,基于支持向量机(SVM:Support Vector Machine)分类算法实现对未爆弹与无害目标的准确分类,同时分析噪声对分类结果的影响。研究结果表明,选用目标响应不同时间的归一化响应以及拟合参数训练出的分类模型,均能有效地对目标进行识别分类,对目标分类准确性达100%,并在实际验证中成功识别出59个目标体。通过实验对比得出,基于特征响应的分类方法计算快,处理方式简单,而基于拟合参数的分类方法则抗干扰能力强,具有更高的准确性。The electromagnetic method has a good response to harmful unexploded ordnance and harmless metal targets,so the false positive detection rate is high,resulting in the subsequent cleaning work is extremely time-consuming.To solve this problem,portable and towed transient electromagnetic detection systems is used to detect multiple unexploded bombs and harmless targets,and estimate the characteristic response of underground targets.According to the estimated electromagnetic characteristics,an accurate classification of unexploded ordnance and harmless targets is achieved based on the SVM(Support Vector Machine)algorithm,and the influence of noise on the classification results is discussed.The results show that the classification model trained by the characteristic responses at different times and the fitting parameters of target response can recognize and classify the targets effectively,the accuracy of target classification has reached 100%,and 59 targets have been recognized successfully in the actual verification.In contrast,the classification method based on characteristic response has fast calculation and simple way of processing,while the classification method based on fitting parameters has strong anti-interference ability and higher accuracy.

关 键 词：电磁法 未爆弹 特征响应 支持向量机 目标分类 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TH763[自动化与计算机技术—计算机科学与技术]