摘要
震动传感器是智能监控传感器系统的重要组成部分。震动信号是一种非线性非平稳信号。相比于传统的时频分析方法,对希尔伯特-黄变换是一种更有效地处理非平稳信号的时频分析方法。将希尔伯特-黄变换引入到车辆分类中,提取震动信号特征信息,利用经验模态分解(EMD)获得车辆行驶引起的地面震动信号的固有模态函数(IMF),通过选取的固有模态函数得到相应的希尔伯特谱,然后在希尔伯特谱的基础上根据谱峰对车辆进行分类。仿真测试结果表明方法具有很高的正确率。
Vibrating sensor is an important component of intelligent monitoring system based on sensors. Vibrating signal is nonlinear and nonstationary. Hilbert - Huang transform is designed to work well for data that are nonstation- ary and nonlinear, and gives results much sharper than any of the traditional analysis methods in time - frequency - amplitude representation. Hilbert - Huang is adopted to vehicle classification. Using the empirical mode decomposition （EMD） , the vibrating signal of vehicle moving is decomposed into a finite and often small number of components, which is a collection of intrinsic mode functions （IMF）. Hilbert spectrum of selected IMFs is calculated and then vehicle classification is done based on spectrum peaks. The results of testing show that it is more efficient with advantage of high accuracy.
出处
《计算机仿真》
CSCD
北大核心
2010年第3期281-285,共5页
Computer Simulation
基金
上海市科委科技攻关项目(07dz15011)
国家863计划项目(2006aa01z216)
关键词
震动信号
希尔伯特-黄变换
希尔伯特谱
车辆分类
Vibrating signal
Hilbert -Huang transform（HHT）
Hilbert spectrum
Vehicle classification
