Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing

Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.