Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search

Geodetic networks provide accurate three-dimensional control points for mapping activities, geoinformation, and infrastructure works. Accurate computation and adjustment are necessary, as all data collection is vulnerable to outliers. Applying a Least Squares (LS) process can lead to inaccuracy over...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-10, Vol.19 (20), p.4535
Main Authors: Koch, Ismael Érique, Klein, Ivandro, Gonzaga, Luiz, Matsuoka, Marcelo Tomio, Rofatto, Vinicius Francisco, Veronez, Maurício Roberto
Format: Article
Language:English
Subjects:
Algorithms
geodetic networks
Geodetics
Global positioning systems
GPS
independent vortices search (ivs)
metaheuristics
Methods
Navigation satellites
Outliers (statistics)
outliers detection
Parameter estimation
Quality control
robust estimation
Satellites
Searching
Signal processing
Vortices
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Summary:Geodetic networks provide accurate three-dimensional control points for mapping activities, geoinformation, and infrastructure works. Accurate computation and adjustment are necessary, as all data collection is vulnerable to outliers. Applying a Least Squares (LS) process can lead to inaccuracy over many points in such conditions. Robust Estimator (RE) methods are less sensitive to outliers and provide an alternative to conventional LS. To solve the RE functions, we propose a new metaheuristic (MH), based on the Vortex Search (IVS) algorithm, along with a novel search space definition scheme. Numerous scenarios for a Global Navigation Satellite Systems (GNSS)-based network are generated to compare and analyze the behavior of several known REs. A classic iterative RE and an LS process are also tested for comparison. We analyze the median and trim position of several estimators, in order to verify their impact on the estimates. The tests show that IVS performs better than the original algorithm; therefore, we adopted it in all subsequent RE computations. Regarding network adjustments, outcomes in the parameter estimation show that REs achieve better results in large-scale outliers’ scenarios. For detection, both LS and REs identify most outliers in schemes with large outliers.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19204535