基于时频原子分解的地雷目标特征提取及分类

利用车载超宽带地表穿透雷达进行大区域地雷探测在军事领域上有重要的应用价值,能否提取稳定一致的地雷特征是其实用化的关键.提出一种基于时频原子分解的地雷目标特征提取和分类方法,该方法以地雷目标的四维散射函数为基础获取二维时频图像,在对时频图像详细分析的基础上,通过时频原子对地雷目标一维距离向剖面进行分解,得到能够完整描述地雷时频域特征的多个原子,将这些原子作为特征向量送入分层分类器.通过实测数据验证,该方法适用于车载超宽带地表穿透雷达探测地雷.同传统基于时域或频域的特征提取算法相比,该方法提取的特征更加稳定,能有效改善地雷探测性能.

Feature extraction and classification of landmine based on time-frequency atom decomposition

It is important in military field to detect landmine using vehicle-mounted ultra band ground penetrating radar,which has the capability to detect landmines over large area.The extraction of steady features has been the crucial factor in the practical use of ultra-band ground penetrating radar.In light of this,a new feature extraction and classification method was proposed based on the time-frequency atom decomposition.The method extracted the two-dimension time-frequency image based on four-dimension scatter function.After analyzing the character of time-frequency image,the time-frequency atoms were used to decompose the one-dimension range profile of landmine.Several time-frequency atoms which could describe the time-frequency character of landmine were sent to hierarchy classifier as features.It was proved by real data that the method was applicable to vehicle-mounted ultra band ground penetrating radar.Compared with the conventional feature extraction algorithms based on time or frequency field,the proposed method can extract steadier feature and improve the performance of landmine detection effectively.