Driftkompensation

Driftkompensation refers to techniques that detect and correct slowly changing biases or drifts in measurement signals or control signals to preserve accuracy over time. In practice, drift can arise from sensor bias, temperature dependence, component aging, reference voltage variations, or drift in feedback loops. If uncorrected, drift degrades long-term accuracy and can lead to erroneous readings or unstable control.

Common approaches include calibration against known references, either periodically or continuously; bias estimation using redundant measurements or state observers; and the use of filters and sensor fusion to separate drift from the desired signal. High-pass filtering can remove slow baseline wander, though this may also attenuate genuine slow dynamics. Kalman filters or complementary filters are frequently employed to estimate and compensate drift in real time, by combining the noisy measurements with a physical model of the system. Temperature compensation, by incorporating temperature sensors and a model of drift versus temperature, is another widespread strategy.

Applications occur across instrumentation, navigation, and process control. In inertial navigation systems, driftkompensation is essential to correct gyroscope and accelerometer biases, often via online bias estimation within an Extended Kalman Filter and by integrating sensor fusion with GPS or magnetometers. In optical sensing and spectroscopy, baseline drift is corrected by adaptive baselines or reference channels. In electronics, drift compensation helps stabilize ADCs, DACs, and voltage references.

Challenges include separating true slow dynamics from drift, avoiding excessive lag, and maintaining robustness under changing conditions. Effective driftkompensation improves measurement reliability and system stability without requiring frequent recalibration.