Blunder Detection Using a Sequential Least Squares Adjustment.
VIRGINIA POLYTECHNIC INST AND STATE UNIV BLACKSBURG
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Blunder detection is a key step in achieving optimal results from a least squares adjustment. One approach to blunder detection is to employ a detection algorithm in a sequential least squares adjustment. A sequential adjustment technique compares the variability of the observations added sequentially to that of the observations already incorporated into the solution. The rejection criterion for observations is based upon an F-statistic. The F-statistic is formed by the ratio of key, independent quadratic forms of residuals corresponding to the observations added sequentially and those already accepted in the solution. Neither an a priori nor an a posteriori reference variance is required for the blunder detection algorithm. The sequential algorithm is computationally efficient because it combines the least squares adjustment and the blunder detection into one step. The sequential least squares blunder detection algorithm is programmable and is tested on measured data sampled from a simple model, a straight line. Author
- Statistics and Probability