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(S...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.展开更多
"边录取、边学习、边建模"是雷达高分辨距离像(high resolution range profile,HRRP)统计识别工程化的方法之一。在独立高斯模型假设下,推导了参数在线学习的公式,根据概率密度值提出一种双门限在线统计识别方法,首先设置门..."边录取、边学习、边建模"是雷达高分辨距离像(high resolution range profile,HRRP)统计识别工程化的方法之一。在独立高斯模型假设下,推导了参数在线学习的公式,根据概率密度值提出一种双门限在线统计识别方法,首先设置门限SA剔除HRRP中的"环值",然后设置门限SB将数据分成几段,从而缓减模型与实时HRRP数据多模特性的失配。基于实测数据的仿真实验证明了本方法的有效性。展开更多
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘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.
文摘"边录取、边学习、边建模"是雷达高分辨距离像(high resolution range profile,HRRP)统计识别工程化的方法之一。在独立高斯模型假设下,推导了参数在线学习的公式,根据概率密度值提出一种双门限在线统计识别方法,首先设置门限SA剔除HRRP中的"环值",然后设置门限SB将数据分成几段,从而缓减模型与实时HRRP数据多模特性的失配。基于实测数据的仿真实验证明了本方法的有效性。
