Sensor Precision
Measurement of the random variation in the output bias of an inertial sensor during a single period of continuous operation. High levels of in-run bias stability indicate that the sensor maintains its calibration over time without drifting significantly. The resulting metric is usually expressed in degrees per hour for gyroscopes.
Drift Characterization
Internal temperature changes and electronic noise contribute to the wandering of the zero point. Evaluating the in-run bias stability allows system designers to estimate the dead reckoning error of a navigation system over a specific mission duration. Allan variance plots are the standard method for extracting this value from raw data.
Analysis of the root mean square error provides a conservative estimate of the performance limits.
Calibration Boundary
Correction algorithms can remove the fixed bias measured at startup but cannot easily compensate for the dynamic changes that occur thereafter. A superior in-run bias stability reduces the frequency of recalibration required to maintain system accuracy. Performance varies significantly between micro-electromechanical systems and fiber optic sensors.
Verification Method
Testing involves logging data from a stationary sensor in a thermally controlled environment for several hours. The resulting in-run bias stability value represents the minimum achievable error floor for the device. Environmental shielding is necessary to prevent external vibrations from corrupting the measurement.
