基于小波理论的行车信息采集与降噪仿真研究

The Traffic Information Acquisition and De-noising Simulation Research Based on Wavelet Theory

为研究基于主动安全的汽车防撞系统,建立运行车辆信息采集系统。信息采集系统的精确度对提高系统实时性能、降低误警率起着重要作用。为降低传感器信号中噪声对信息采集系统精度和可靠性的干扰,在信号进入控制系统安全模型前预先对其进行阈值降噪处理。选取适当小波基函数评价指标,根据计算结果,选取合适小波基,新建一阈值函数,并在Matlab平台进行仿真测试。仿真结果显示,基于小波阈值的降噪方法有效地保留原信号特征并去除噪声,对行车信息降噪处理是可行的。

In order to research the active anti-collision automotive system, the traffic information acquisition system was established. The accuracy of system plays an important role in improving real-time performance and reducing the false alarm rate. To reduce the influence of the noise in sensor signal to the system accuracy and reliability, the signal with wavelet threshold is denoising processed before modeling. Appropriate wavelet base function evaluation index is selected. According to calculation, suitable wavelet base function is chosen, and a new threshold function is built, simulation test on matlab platform is implemented. The simulation results show that, the wavelet threshold de-noising method can retain the original signal characteristics and remove noise more efficiently, is feasible to traffic information de-noising.