基于MFCC和支持向量机的装甲车辆识别研究

Research on armored vehicle classification based on MFCC and SVM

针对地面战场装甲车辆目标的被动声识别问题,选取具有代表性的两类坦克和两类履带式装甲车作为识别对象,以卡车噪声和雷声信号作为干扰项,对噪声信号进行预加重、分帧加窗、计算功率谱后输入梅尔滤波器组,得到噪声信号的MFCC并计算平均值作为特征值构建特征向量,以支持向量机作为分类器,建立了一种装甲车辆识别方法,该方法对目标的识别率可达95%以上。研究结果表明,该方法对坦克及装甲车辆的识别效果较好,可以有效抵抗战场非战斗目标噪声信号的影响,为战场决策提供准确信息。

In order to identify the ground battlefield armored vehicle target through passive acoustic recognition ,select representative objectives include two kinds of tanks and two kinds of crawler armored vehicle as the noise acquisition ,use the signal of truck and thunder as interference term ,the noise signal are pre-weighted ,framed ,calculated the power spectrum and then entered the M el filter bank ,using mean value of noise signal＆#39;s M FCC as eigenvalue in order to con-struct feature vector ,with support vector machine as classifier ,establish an armored vehicle classification method which classification rate can reach more than 95% .Research indicates that this method is adaptable to the classification of tank and armored vehicle and can effectively resist the impact of non-combat target＆#39;s noise signal on battlefield ,provided right information for battlefield decisions .