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1.
Sensors (Basel) ; 19(20)2019 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-31635349

RESUMO

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.

2.
R Soc Open Sci ; 2(4): 140417, 2015 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26064634

RESUMO

The Helmert-blocking technique is a common approach to adjust large geodetic networks like Europeans and Brazilians. The technique is based upon a division of the network into partial networks called blocks. This way, the global network adjustment can be done by manipulating these blocks. Here we show alternatives to solve the block system that arises from the application of the technique. We show an alternative that optimizes its implementation as the elapsed processing time is decreased by about 33%. We also show that to insert observations into an adjusted network it is not necessary to readjust the whole network. We show the formulae to insert new observations into an adjusted network that are more efficient than simply readjusting the whole new network.

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