What is linearization in sensor signal processing?

What is linearization in sensor signal processing?

• A. The process of smoothing out noise in the sensor signal.
• B. The process of converting a digital signal back to analog.
• C. The process of calibrating a sensor to a fixed bias.
• D. The process of mapping a nonlinear sensor response to a linear output using calibration curves or piecewise linear segments.

Answer: (D)

Linearization means transforming a nonlinear sensor response into a linear one so that the output changes in proportion to the measured quantity over the operating range. Many sensors don’t respond in a perfectly straight line because of their physics, saturation, or varying sensitivity. To make the signal easy to interpret and use in control or data processing, we apply calibration curves or piecewise linear segments that invert or approximate the sensor’s transfer function, so a given output corresponds to a proportional input value. This approach keeps the relationship simple for scaling, interpolation, and decision-making in the system. It’s not about smoothing noise (that’s filtering), converting digital to analog (that’s a DAC activity), or simply offsetting a bias (that’s offset calibration).

Linearization means transforming a nonlinear sensor response into a linear one so that the output changes in proportion to the measured quantity over the operating range. Many sensors don’t respond in a perfectly straight line because of their physics, saturation, or varying sensitivity. To make the signal easy to interpret and use in control or data processing, we apply calibration curves or piecewise linear segments that invert or approximate the sensor’s transfer function, so a given output corresponds to a proportional input value. This approach keeps the relationship simple for scaling, interpolation, and decision-making in the system. It’s not about smoothing noise (that’s filtering), converting digital to analog (that’s a DAC activity), or simply offsetting a bias (that’s offset calibration).