应用于超声测距的小波变换滤波算法

有效的滤波算法是提高超声测距精度的关键之一。小波变换具有时频联合分析的能力,采样点处的各级小波系数反映了其频率成分的分布情况。该文提出了一种基于小波变换的超声回波滤波算法。对原始数据进行离散二进小波变换,然后将各点的小波系数同理想回波信号的小波系数进行相关运算,利用得到的相关系数区分噪声和回波所在区段,然后对噪声的小波系数进行收缩处理,从而实现滤波。利用该算法对自制的超声测距装置采集到的回波数据进行了滤波处理。结果表明:其滤波效果要优于经典的小波阈值法,信号信噪比提高了6～9dB,数据中混有的大幅值噪声得到了有效抑制。

Wavelet denoising algorithm for ultrasonic ranging

Effective de-noising methods are necessary in ultrasonic ranging to improve the measurement precision and stability.A de-noising algorithm based on wavelet transforms is presented in this paper.Wavelet transforms are a time-frequency analysis method that indicates the frequency distribution at each sampling point.This model analyzes the sampled data with a stationary wavelet transform(SWT) and correlates the wavelet coefficients with those of an ideal echo signal.The correlation result was then used to identify and eliminate the noise.This algorithm was evaluated on signals acquired with an in-house ultrasonic ranging system with better performance compared to the classic wavelet threshold method.The signal-noise ratio(SNR) is enhanced by 6 to 9 dB and large amplitude noise is effectively suppressed.