A Practical Strategy For Detecting Multiple Gross Errors

Survey measurements are subject to random, systematic and gross errors. In practice, it is assumed that measurements are random variables, follow the normal distribution and have redundancy. The method of least squares estimation (ESE) is commonly used to process the redundant measurements. In the p...

全面介紹

Saved in:
書目詳細資料
主要作者: Setan, Halim
格式: Article
語言:English
出版: Fakulti Ukur dan Harta Tanah 1996
主題:
在線閱讀:http://eprints.utm.my/id/eprint/4880/1/APractical.pdf
http://eprints.utm.my/id/eprint/4880/
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結:Survey measurements are subject to random, systematic and gross errors. In practice, it is assumed that measurements are random variables, follow the normal distribution and have redundancy. The method of least squares estimation (ESE) is commonly used to process the redundant measurements. In the presence of gross errors, the results of ordinary ESE are corrupted. Consequently, an interactive post-LSE technique of robustified LSE (RLSE) is introduced for the detection of multiple gross errors in uncorrelated surveying data. In RESE. the locations and magnitudes of such errors are recovered simultaneously, and their effect on the solution are areatlv reduced.