Integrated Navigation Test 2 Practice

Fault Detection and Exclusion is the method designed to keep navigation results trustworthy by spotting measurements that don’t fit with the rest and removing them from the solution. In practice, the receiver compares each measurement against what the current estimate predicts or against the consistency of all measurements together. If one measurement behaves anomalously—exceeding a threshold or producing an outlier in residuals—it’s excluded, and the estimate is recomputed with the remaining data. This direct exclusion step is what preserves integrity, especially when a single faulty reading could skew the result.

Other options don’t fit as precisely. The Kalman Filter estimates the state by blending a model with measurements, but it doesn’t inherently identify and drop faulty measurements; outliers can bias the update unless extra outlier-handling is added. RMS is simply a measurement of average error magnitude, not a procedure to detect or remove bad data. RAIM monitors integrity using redundant observations and can indicate faults, but the explicit exclusion of a faulty measurement to maintain the solution’s integrity is the role of Fault Detection and Exclusion.