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Treatment of Geodetic Survey Data as Fuzzy Vectors : Volume Xl-3/W3, Issue 1 (19/08/2015)

By Navratil, G.

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Book Id: WPLBN0004014912
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Treatment of Geodetic Survey Data as Fuzzy Vectors : Volume Xl-3/W3, Issue 1 (19/08/2015)  
Author: Navratil, G.
Volume: Vol. XL-3/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Heer, E., Hahn, J., & Navratil, G. (2015). Treatment of Geodetic Survey Data as Fuzzy Vectors : Volume Xl-3/W3, Issue 1 (19/08/2015). Retrieved from

Description: Department for Geodesy and Geoinformation, TU Vienna, Austria. Geodetic survey data are typically analysed using the assumption that measurement errors can be modelled as noise. The least squares method models noise with the normal distribution and is based on the assumption that it selects measurements with the highest probability value (Ghilani, 2010, p. 179f). There are environment situations where no clear maximum for a measurement can be detected. This can happen, for example, if surveys take place in foggy conditions causing diffusion of light signals. This presents a problem for automated systems because the standard assumption of the least squares method does not hold. A measurement system trying to return a crisp value will produce an arbitrary value that lies within the area of maximum value. However repeating the measurement is unlikely to create a value following a normal distribution, which happens if measurement errors can be modelled as noise. In this article we describe a laboratory experiment that reproduces conditions similar to a foggy situation and present measurement data gathered from this setup. Furthermore we propose methods based on fuzzy set theory to evaluate the data from our measurement.



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