ADC (Analog-to-digital converter) is an important component in measuring instruments that converts the analog signals to digital form. The actual effective vertical resolution of ADC is one of the most important indicators for evaluating the ADC performance. However, it is restricted by various factors such as external disturbance, environmental interference, clock jitter, transmission delay, etc. These problems lead to a lower ADCs’ effective resolution than the ideal value. This paper proposes an improved algorithm dedicated to decrease errors in measured signals. In this algorithm, we make Multiwavelet decomposition to get the probability density function of measured signal first, then use singular value decomposition to remove the noise related frequency characteristic values in the multiwavelet basis coefficients, next create a Bayesian fusion iteration process to optimize the probability density function and build a confidence interval for elimination of the noise data, and finally the signal is reconstructed and average filtered for output. Through simulation experiments we show an improvement of the vertical resolution and SNR (signal-to-noise ratio) in comparison to the conventional averaging filter, high resolution acquisition and oversampling methods.

中文翻译：

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一种高分辨率ADC的多小波概率密度估计算法

ADC（模数转换器）是测量仪器中将模拟信号转换为数字形式的重要部件。ADC的实际有效垂直分辨率是评估ADC性能的最重要指标之一。但是，它受到外界干扰、环境干扰、时钟抖动、传输延迟等多种因素的制约，这些问题导致ADC的有效分辨率低于理想值。本文提出了一种改进的算法，专门用于减少测量信号中的误差。该算法首先进行多小波分解，得到被测信号的概率密度函数，然后利用奇异值分解去除多小波基系数中与噪声相关的频率特征值，接下来创建贝叶斯融合迭代过程来优化概率密度函数并建立置信区间以消除噪声数据，最后重建信号并平均滤波输出。通过模拟实验，我们展示了与传统平均滤波器、高分辨率采集和过采样方法相比，垂直分辨率和 SNR（信噪比）的改进。