Digital methods to quantify sensor output uncertainty in real time.

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 pre-stored 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.