Sensordrift

Sensordrift is a term used to describe the gradual deviation of a sensor's output from the true value over time, caused by changes in the sensor's characteristics rather than random measurement noise. It is commonly observed as bias drift, scale-factor drift, or nonlinear drift and can affect any sensor type, including inertial measurement units, optical sensors, temperature and chemical sensors, and electronic transducers.

Causes include aging of components, temperature fluctuations, voltage supply variations, humidity, mechanical stress, radiation exposure, and installation conditions. Some drift is deterministic and predictable through a model (for example temperature-dependent bias), while other drift is stochastic and harder to forecast. Drift can accumulate, especially in integration or fusion tasks, leading to steadily increasing error if uncorrected.

Mitigation strategies include regular calibration against known references, in-situ or self-calibration routines, and the use of sensor fusion algorithms that combine multiple measurements to cancel drift (for example Kalman filter or complementary filter). Drift models may be incorporated to compensate measurements, with parameters updated online. Redundancy, thermal management, and selecting sensors with low long-term drift are also common approaches.

In practice, sensordrift affects fields such as navigation, robotics, aerospace, and industrial automation, where accurate long-term measurements are essential. The impact depends on the measurement modality, the time horizon, and whether drift is systematic or random. Clear documentation of drift characteristics and calibration history is important for data quality and traceability.