Table of Contents
Sensors often exhibit non-linear behavior, meaning their output does not change proportionally with the measured quantity. Understanding these non-linearities is essential for accurate data collection and analysis. Calibration techniques are used to identify and correct these deviations, improving sensor performance.
Types of Sensor Non-Linearities
Non-linearities can be categorized into several types, including:
- Offset errors: deviations at zero input.
- Gain errors: incorrect slope of the output response.
- Hysteresis: output depends on the direction of input change.
- Drift: gradual change over time.
Calibration Techniques
Calibration involves comparing sensor output with known reference standards. Techniques include linear calibration, polynomial calibration, and piecewise calibration. These methods help model the sensor’s response accurately across its measurement range.
Correction Methods
Once non-linearities are characterized, correction algorithms can be applied. Common methods include:
- Lookup tables: storing correction values for specific input ranges.
- Mathematical models: applying polynomial or exponential functions.
- Real-time filtering: smoothing data to reduce errors.