Which integrity monitoring technique uses redundancy to detect outliers in navigation measurements?

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Multiple Choice

Which integrity monitoring technique uses redundancy to detect outliers in navigation measurements?

Using redundancy among navigation measurements to verify consistency and catch outliers is what RAIM does. It works by looking at all available satellite measurements and testing whether any single measurement is inconsistent with the rest, using methods like leave-one-out checks or residual (often chi-squared) tests. If a measurement doesn’t fit with the others, RAIM flags it as an outlier and can exclude that satellite to maintain an accurate and trustworthy position solution. Kalman filtering relies on a model and residuals for anomaly detection but isn’t defined by redundancy across measurements. Pseudorange smoothing focuses on noise reduction over time, not direct outlier detection. Fault Detection and Exclusion is related, but the feature most closely described by using redundancy to detect outliers is RAIM.

Using redundancy among navigation measurements to verify consistency and catch outliers is what RAIM does. It works by looking at all available satellite measurements and testing whether any single measurement is inconsistent with the rest, using methods like leave-one-out checks or residual (often chi-squared) tests. If a measurement doesn’t fit with the others, RAIM flags it as an outlier and can exclude that satellite to maintain an accurate and trustworthy position solution. Kalman filtering relies on a model and residuals for anomaly detection but isn’t defined by redundancy across measurements. Pseudorange smoothing focuses on noise reduction over time, not direct outlier detection. Fault Detection and Exclusion is related, but the feature most closely described by using redundancy to detect outliers is RAIM.

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