基于EEMD穿墙雷达人的运动模式识别

根据穿墙雷达动目标探测中人的运动多普勒信号属于非线性、非平稳信号的特点,分别采用经验模式分解(EMD)和整体平均经验模式分解(EEMD)将人.5种运动的多普勒信号分解为一系列本征模式函数(IMF).采用支持向量机(SVM)学习算法,将两种方法分解后的各IMF能量占总能量的百分比作为支持向量机分类器的特征向量进行模式识别...

Human Activity Classification Based on EEMD Using a Support Vector Machine

For the moving target detection using the through wall radar, according to the fact that the Doppler signals of the human activities are nonlinear and non-stationary, the ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) are used respectively to decompose five different activities Doppler signals into a set of instinct mode functions (IMF). The five different activities include the human standing still, standing with arms waving, stepping forward and backward, walking and running. A support vector machine (SVM) is trained using the energy ratio of each IMF to the total IMFs as the features to classify the activities. The relationship between the classification accuracy with the number of the features is analyzed and the classification accuracy comparison using two different decomposition methods is given. Because the EEMD can eliminate the mode mixing problem existed in EMD and each IMF obtained from the EEMD has a clear physical meaning, the classification accuracy using the EEMD is found to be more than 94%, higher than the one using the EMD.