  This article presents an algorithm for reducing measurement uncertainty of
one physical quantity when given oversampled measurements of two physical
quantities with correlated noise. The algorithm assumes that the aleatoric
measurement uncertainty in both physical quantities follows a Gaussian
distribution and relies on sampling faster than it is possible for the
measurand (the true value of the physical quantity that we are trying to
measure) to change (due to the system thermal time constant) to calculate the
parameters of the noise distribution. In contrast to the Kalman and particle
filters, which respectively require state update equations and a map of one
physical quality, our algorithm requires only the oversampled sensor
measurements. When applied to temperature-compensated humidity sensors, it
provides reduced uncertainty in humidity estimates from correlated temperature
and humidity measurements. In an experimental evaluation, the algorithm
achieves average uncertainty reduction of 10.3 %. The algorithm incurs an
execution time overhead of 5.3 % when compared to the minimum algorithm
required to measure and calculate the uncertainty. Detailed instruction-level
emulation of a C-language implementation compiled to the RISC-V architecture
shows that the uncertainty reduction program required 0.05 % more instructions
per iteration than the minimum operations required to calculate the
uncertainty.
