基于改进阈值小波算法的汽车轮速信号处理

Automobile wheel speed signal processing based on wavelet algorithm of improved threshold

在汽车制动过程中,轮速信号中的噪声对制动有着直接的影响,运用小波理论可以对轮速信号中的噪声进行去噪处理。噪声的幅值随着小波变换尺度的增加会逐渐减小,而信号的幅值与小波变换的尺度变化无关。在Donoho的软、硬阈值去噪方法基础上,提出了一种新的阈值函数量化法,该方法克服了硬阈值法不连续性和软阈值法有偏差的缺点,并把它们应用在汽车轮速信号的去噪上。新的阈值函数具有物理意义清晰、表达式简单等优点。实际信号处理结果表明,这种经改进的方法可以有效地去除噪声干扰,在信噪比指标上也明显优于常用的软、硬阈值去噪算法。

In the course of vehicle braking, the noise in wheel speed signal has direct influence on vehicle braking; wavelet theory can be used to remove the noise in wheel speed signal. The amplitude of the noise decreases with the increase of the scale of wavelet transform, but the amplitude of the signal has no relation to the wavelet transform scale. Based on the soft and hard thresholding methods put forward by Donoho, a new thresholding function method is proposed ; this method can overcome the discontinuity of the hard threshold and the offset of the soft threshold, and has been applied to automobile wheel speed signal denoising. The new threshold function has many advantages over Donoho＇ s soft and hard threshold functions, and is clear in physical meaning and simple in expression. Signal processing results show that the improved method is effective in removing noise, and gives better SNR performance than the soft and hard thresholding methods.