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超声测距的精度主要取决于飞行时间的提取精度,由于受噪声影响,接收信号的始点位置通常无法精确提取.传统方法常采用设计滤波器降噪,然而由于滤波器的参数无法随超声信号变化而改变,导致其适应性较差.本文提出了一种自相关小波阈值去噪的自适应去噪方法,通过计算各层小波细节分量与近似分量的相关性系数,自动确定最优分解层数,并通过采集分析噪声信号的小波分量,选择最优去噪阈值,达到了良好的去噪效果,增强了回波信号的信噪比,极大地提高了回波飞行时间的提取精度.经实验验证,信噪比可提高6~7 d B,在1 900 mm的范围内测量误差小于0.3 mm,测量不确定度小于0.17 mm.与传统方法相比,本方法具有适应性强、始点识别率高、测距精确等优点.
The accuracy of ultrasonic ranging mainly depends on the accuracy of the extraction of flight time, and the initial position of the received signal can not be accurately extracted because of the influence of noise.Traditional methods often use design filters to reduce noise, however, since the parameters of the filters can not change with the ultrasonic signals And change, resulting in its poor adaptability.This paper presents a self-correlation wavelet denoising adaptive denoising method, by calculating the correlation coefficient between the wavelet detail and approximate components of each layer, automatically determine the optimal decomposition level , And through the acquisition and analysis of the wavelet component of the noise signal, select the optimal denoising threshold, to achieve a good noise reduction effect, enhance the signal-to-noise ratio of the echo signal, which greatly improves the echo flight time extraction accuracy. The signal to noise ratio can be increased by 6-7 d B, the measurement error is less than 0.3 mm in the range of 1 900 mm and the measurement uncertainty is less than 0.17 mm.Compared with the traditional method, this method has the advantages of strong adaptability, initial recognition rate High, accurate ranging and so on.