A Closer Look at the Tau Criterion as a Method of Blunder Detection within a Differential Leveling Network Least Squares Adjustment

摘要

Data collection is a very tedious and repetitive process. No matter how carefully the data are collected, mistakes are a part of life during field collections. Even during the most careful data collecting processes, it is easy to overlook something or make a simple mistake, which can introduce a blunder in the data. This paper analyzes blunder detection using the Tau criterion and researches its use to isolate and eliminate blunders from the data. A software program that was previously written to perform a least squares adjustment on a differential leveling network has been enhanced to incorporate the Tau criterion as a method for the detection of blunders within the data.