sensormodeller

Sensormodeller is a general concept in engineering and data science that refers to the creation of mathematical or statistical models of sensor behavior. A sensormodeller describes how a sensor's nominal measurement relates to the quantity being observed and how various imperfections affect the reading, including noise, bias, drift, nonlinearity, saturation, and time delay. The purpose is to enable interpretation, simulation, calibration, and fusion of sensor data across different platforms and conditions.

A sensormodeller typically comprises a model of the observation process, a dynamic or static relation between the input quantity and the observed signal, and a method for estimating model parameters from data. Models can be physics-based, capturing the underlying sensor physics; data-driven, derived from calibration data using regression or machine learning; or hybrid, combining both approaches. Common model forms include static transfer functions, dynamic state-space or ARX models, and probabilistic models that quantify uncertainty.

Applications include sensor calibration to improve accuracy, sensor fusion in robotics or autonomous systems, simulation for design and testing, fault detection and health monitoring, and compensating measurements for environmental influences. Validation uses metrics such as RMSE, MAE, R^2, or likelihood-based criteria, often with cross-validation and uncertainty quantification.

Advances in sensormodeller practice emphasize online or adaptive modelling, uncertainty propagation, and integration with multi-sensor networks, enabling robust performance in changing conditions.