中文摘要：

利用车载前视超宽带地表穿透雷达进行大区域地雷探测是探雷的发展方向,目前制约车载前视超宽带地雷穿透雷达探雷实用化的关键问题是难以提取一致性较好的地雷目标特征。文中以地雷目标的斜距-方位-频率-反射角四维散射函数为基础,提取二维时频域特征图像。然后对时频图像进行特征选择,并实现目标和杂波的分类。为降低分类器训练误差以提高其泛化性能,将特征选择和分类器设计有机结合。同时为保证地雷探测过程中具有恒定的探测率,在分类器训练过程中以恒检测率下的虚警率作为代价函数,通过实测数据验证,该方法适用于车载前视超宽带地表穿透雷达,最终得到的分类器能提供恒定的检测率,并且具有很好的泛化性能。

英文摘要：

It was the trend of landmine detection using vehicle-mounted ultra-band ground penetrating radar,which had the capability to detect landmines over large area.The difficulty in ultra-band ground penetrating radar was the extraction of steady features. In this paper,the two-dimension time-frequency image was extracted based on the five-dimension scatter function,which was constructed of range,azimuth,frequency,and angle of reflection.And the feature selection and classification algorithm were based on the time-frequency images of targets and clutters.To decrease the training error and increase the generalization capability of classifier,the feature selection was involved in the design of classification.And to guarantee the probability of landmine detection,the false rate was regarded as cost function of classification through the training of classifier.It was proved by real data that the method was applicable to vehicle-mounted ultra-band ground penetrating radar.The classifier finally generated has a good generation capability and can offer constant probability of detection.