Gross-Error Detection in GNSS Networks Using Spanning Trees

Abstract

Many methods and techniques have been developed to detect gross errors in geodetic measurements, but none seems to have prevailed. Statistical tests and robust methods are the most common approaches for detecting outliers in geodetic measurements. Least-squares adjustment and iterative attitudes are the essence of those methods. In this paper, closing loops in Global Navigation Satellite System (GNSS) networks are used to detect gross errors. Spanning trees are used to define a set of independent loops in the network. Careful examination of the misclosure of loops assists in defining the faulty vectors. The method is very effective and delivers another alternative for outlier detection without using adjustment computation and statistical tests. The method of outlier detection by means of spanning trees is presented and tested against well-known methods like convectional statistical tests (w-test,