Abstract:

In the Baarda and Pope methods, frequently used to detect outliers, the total bias additive model estimates the outliers. Unlike the traditional method, outliers can be modeled as additional unknowns. With this approach, all observations are viewed as suspicion. It is, therefore, essential to consider all possible combinations. It is based on evaluating the combinations from the number of observations in the network (n elements) to the highest possible number of outliers that can occur. This study aims to apply the approach of modeling the outliers as unknown in the geodetic network and to evaluate the results' reliability with the comparison values from different distributions. Also, the effectiveness of traditional and proposed approach in the masking and swamping effect scenarios were investigated. For this purpose, mean success rates for detecting small magnitude outliers were calculated by simulating a geodetic leveling network. According to the results, modeling the outliers as unknown is approximately 30-40% more reliable than traditional methods. Among the three distributions used in the proposed method, the standard normal distribution and Tau distribution have the highest success rates. Additionally, it has been noticed that both the conventional and proposed methods produce misleading outcomes in cases involving swamping and masking effects.

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