Which are common bias sources in accelerometers and gyroscopes in an INS?

• A. Only bias and noise.
• B. Vibration and humidity are the primary biases.
• C. Bias (offset), scale factor error, misalignment, bias drift, temperature dependence, and noise.
• D. Only temperature dependence.

Answer: (C)

In an INS, accuracy hinges on understanding how sensor outputs deviate from reality due to multiple systematic and random errors. The key bias-related error sources include offset (a constant reading added to the true value), scale factor error (the output isn’t exactly proportional to the input), and misalignment (the sensor axes aren’t perfectly aligned with the reference axes, causing cross-axis effects). In practice, the offset can drift over time—this is bias drift—driven by aging, temperature, and other factors. Temperature dependence itself is a major cause of bias shifting, since sensor characteristics often change with temperature. Noise is the random component that crowds the measurement around its biased value, affecting precision and long-term stability.

That is why the best answer lists bias (offset), scale factor error, misalignment, bias drift, temperature dependence, and noise. It captures the full set of common contributors to biased sensor behavior in accelerometers and gyroscopes.

The other options are too narrow. One omits scale, misalignment, drift, and temperature effects. Another suggests vibration and humidity as primary biases—humidity isn’t typically a bias source in accelerometers/gyros, and humidity isn’t usually treated as a dominant bias; vibration relates more to dynamic noise and sensor excitation. The last option focuses only on temperature dependence, missing the other substantial bias sources.