Digital methods to quantify sensor output uncertainty in real time

Digital methods to quantify sensor output uncertainty in real time - Communications Engineering

Orestis Kaparounakis and colleagues report a real-time method to track uncertainty in sensor outputs caused by low-precision calibration data. This could help sensing systems make better use of measurements when uncertainty changes from case to case.

Nature Publishing Group UKOrestis Kaparounakis

Abstract

Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characterization of sensor output uncertainty is important for reliable data interpretation. Here, we present a method for real-time on-device dynamic uncertainty quantification for sensor outputs which depend on prestored calibration data. We show how sensor calibration compensation equations (essential in advanced sensing systems) propagate uncertainties resulting from the quantization of calibration parameters to the sensor output. We use a low-cost thermal sensor as a motivating example and show these ideas are practical and possible on actual embedded sensor systems by prototyping them on two commercially-available uncertainty-tracking hardware platforms with average power dissipation 16.7 mW and 147.15 mW. These achieve 42.9× and 94.4× speedup compared to the equal-accuracy Monte Carlo computation (the status quo). We present a proof-of-usefulness edge-detection application over ten test scenes where accuracy and precision show average improvement by 4.97 and 40.25 percentage points, respectively, trading off sensitivity. Another application example examines four different calibration-data storage scenarios and compute that a 48% increase in memory yields 75% smaller uncertainty metrics over the baseline. Our method enables better decision-making in critical applications where sensor data reliability is paramount.

Cite as:

Kaparounakis, O., Stanley-Marbell, P. Digital methods to quantify sensor output uncertainty in real time. Commun Eng 5, 135 (2026). https://doi.org/10.1038/s44172-026-00679-4

BibTeX:

@article{kaparounakis2026digital,
  title={Digital methods to quantify sensor output uncertainty in real time},
  author={Kaparounakis, Orestis and Stanley-Marbell, Phillip},
  journal={Communications Engineering},
  year={2026},
  publisher={Nature Publishing Group UK London}
}```