摘要
采用Bartlet平均周期图、小波变换和经验模态分解方法分别提取信号的频段能量值特征、IMF能量值特征,重点对IMF能量值特征进行特征优选。通过设计BP神经网络分类器,对实测的四类舰船目标的辐射噪声信号进行测试,取得了较好的识别效果。
Bartlet average periodogram, wavelet transforyn and empirical mode decomposition method were used to extractthe signal’s frequency band energy and intrinsic mode function energy feature, the feature selection of IMF energy is the keypoint. Then it designs the BP neural network classifier for testing the radiated noise signals of four kinds of skip targets, andgets a good recognition effect.
出处
《指挥控制与仿真》
2016年第2期60-65,共6页
Command Control & Simulation
关键词
小波变换
经验模态分解
特征优选
BP神经网络分类器
wavelet transfomi
empirical mode decomposition
feature selection
BP neural network classifier
