关键词: 自适应滤波, 加权滑动平均, 相位距离, 幅值距离

Abstract: For the peak signals such as spectrogram, electrocardiogram, light spot image of linear CCD, it is difficult to denoise the image completely and preserve the peaks’ features effectively during filtering. The location, amplitude, area, and edge steepness of the signal peak are inevitably affected. Based on the analysis of conventional Weighted Moving Average（WMA） filtering algorithm, a new bivariate distance weight concept based on amplitude & phase is proposed. It takes into account not only the phase correlation but also the amplitude correlation among the sampled pixels during WMA computation, and solves the problem pushing up the data on the bottom of the peak. By dynamically adjusting one or both of the phase distance threshold and the amplitude distance threshold, the problem pulling down the data on the top of the peak is also solved. Finally, an adaptive moving average filtering algorithm weighting on amplitude & phase distance is developed. This new algorithm is applied to the vision testing system of vehicle wheel alignment parameters based on the linear CCD image, and improves the sub-pixel measurement precision of the light spot position from 0.5 pixels to 0.03 pixels.

Key words: adaptive filtering, weighted moving average, phase distance, amplitude distance