  Most sensor calibrations rely on the linearity and steadiness of their
response characteristics, but practical sensors are nonlinear, and their
response drifts with time, restricting their choices for adoption. To broaden
the realm of sensors to allow nonlinearity and time-drift in the underlying
dynamics, a Bayesian inference-based nonlinear, non-causal dynamic calibration
method is introduced, where the sensed value is estimated as a posterior
conditional mean given a finite-length sequence of the sensor measurements and
the elapsed time. Additionally, an algorithm is proposed to adjust an already
learned calibration map online whenever new data arrives. The effectiveness of
the proposed method is validated on continuous-glucose-monitoring (CGM) data
from an alive rat equipped with an in-house optical glucose sensor. To allow
flexibility in choice, the validation is also performed on a synthetic blood
glucose level (BGL) dataset generated using FDA-approved virtual diabetic
patient models together with an illustrative CGM sensor model.
