A Software Level Calibration Based on Bayesian Regression for a Successive Stochastic Approximation Analog-to-Digital Converter System
针对逐次随机逼近ADC两种模式输出需软件校正的问题，提出基于贝叶斯回归的增量学习方法，通过不确定性估计选择数据，实验验证了性能。
Recently, a novel low-power high-precision analog-to-digital converter (ADC) called the successive stochastic approximation ADC has been proposed which has two kinds of outputs from different modes, and which requires a software-level error correction method of combining them into a high-precision total output. From the practical viewpoint, we propose an error correction method based on the Bayesian regression with an incremental learning, in which additional data are successively selected according to the uncertainty of the corresponding predictive total output, and the uncertainty is approximately estimated by evaluating the upper bound of the standard deviations of the Bayesian predictive distributions of the outputs in each block of a partition of the all data set. Through numerical experiments, we verify the performance of the proposed method.