sensormodel

A sensor model is a mathematical representation of how a physical sensor converts an input quantity into a measurable signal. It captures ideal transduction behavior as well as non-ideal effects such as gain error, bias, nonlinearity, saturation, noise, and temporal dynamics. Sensor models are used in analysis, simulation, calibration, and data fusion to interpret measurements and to predict how sensors respond under varying conditions.

A typical model separates the signal path into an input-to-output mapping (the transducer or observation model) and a description of imperfections. Static models describe a fixed input-output relation, while dynamic models also describe how measurements change over time. Common components include a transfer function or state-space representation, measurement noise with a statistical distribution, and possible environmental or operational dependencies (temperature, aging, illumination).

Modeling approaches vary by application. Linear, time-invariant models y = Hx + w assume constant gain and no memory. Nonlinear or time-varying models handle saturation or nonlinearity. Dynamic models use state estimation formalisms such as state-space or autoregressive structures. Calibration or system identification procedures estimate parameters from reference measurements, often incorporating uncertainty quantification and drift compensation.

Sensor models enable tasks such as sensor fusion, simulation for algorithm development, and robust control. Examples include the pinhole camera model for vision systems, LIDAR and radar range and intensity models, and inertial measurement unit models that account for bias and noise. Each domain defines its own conventions for units, coordinate frames, and noise statistics.

Limitations include model mismatch, limited domain validity, and the challenge of capturing all environmental influences. Accurate uncertainty descriptions are essential for reliable inference. See also sensor fusion, calibration, and system identification.