In the calibration process, which item is calculated to assess calibration quality?

• A. Computational uncertainty
• B. Azimuth error
• C. Range error
• D. Time drift

Answer: (A)

When evaluating calibration quality, the key is to quantify how confident you are in the calibrated results. During calibration, you fit a model to reference data and propagate all sources of error—measurement noise, model inaccuracies, and numerical effects—through the computation to get not just parameter estimates but a quantified uncertainty in those estimates. This uncertainty, often termed computational uncertainty, serves as the overall measure of how reliable the calibration is. It directly tells you whether the calibrated values meet tolerance limits and how much the results might vary in practice.

Azimuth error, range error, and time drift describe specific deviations or instability in particular outputs or conditions, but they are outcomes you might observe after calibration rather than a single, comprehensive metric of calibration quality. They can indicate issues, but they don’t summarize the confidence in the calibration the way computational uncertainty does.