Automatic target recognition of moving target based on empirical mode decomposition and genetic algorithm support vector machine
来源期刊：中南大学学报(英文版)2015年第4期
论文作者：ZHANG Jun OU Jian-ping ZHAN Rong-hui
文章页码：1389 - 1396
Key words：automatic target recognition (ATR); moving target; empirical mode decomposition; genetic algorithm support vector machine
Abstract: In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition (EMD) and support vector machine (SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions (IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm (GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
ZHANG Jun(张军), OU Jian-ping(欧建平), ZHAN Rong-hui(占荣辉)
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
Abstract:In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition (EMD) and support vector machine (SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions (IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm (GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
Key words:automatic target recognition (ATR); moving target; empirical mode decomposition; genetic algorithm support vector machine
