GNSS Differential Positioning by Robust Estimation

Baselga, Sergio; García-Asenjo, Luis

doi:10.1061/(asce)0733-9453(2008)134:1(21)

Cited by 24 publications

(12 citation statements)

References 6 publications

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“…Considering that a robust estimator is optimally computed as a global optimization problem and that the minimum L 1 -norm (please note that there is a conflict of notation here, L 1 -norm refers to the minimum sum of absolute residuals and has not to be confused with the GPS frequency L 1 ) has been shown to be a successful robust estimator (e.g. Fuchs 1982, Harvey 1993, Baselga and García-Asenjo 2008a, 2008b we will solve for the coordinates implicit in (1) after a few manipulations along with the statistic condition of minimum L 1 -norm.…”

Section: Functional Model and Solutionmentioning

confidence: 99%

Submillimetric GPS distance measurement over short baselines: noise mitigation by global robust estimation

Baselga¹,

García-Asenjo²,

Garrigues³

2014

Meas. Sci. Technol.

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Abstract. The potential use of GPS for precise length determination is currently a topic of extensive research. A prior work was dedicated to submillimetric length determination over short baselines under ideal conditions of data availability and clear environment. This paper presents a new computation method suited to the less favourable working conditions that are usually encountered in practice, which is based on both robust estimation theory and the use of an ambiguity free estimation method. As the experimental comparisons with the standard procedure based on least squares ambiguity determination show, it provides more stable values and permits to obtain results significant to the submillimetre level with time spans of the order of a few hours.

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Section: Functional Model and Solutionmentioning

confidence: 99%

Submillimetric GPS distance measurement over short baselines: noise mitigation by global robust estimation

Baselga¹,

García-Asenjo²,

Garrigues³

2014

Meas. Sci. Technol.

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“…Finally, let us apply Global Robust Estimation to this example, in a similar fashion as we did in Baselga and García-Asenjo (2008) for coping with ionospheric delays. In this case we have followed the Simulated Annealing method as sketched in Section 3.3.…”

Section: Examplementioning

confidence: 99%

“…Since this initial solution may be highly contaminated by the presence of undesirable errors and the correct solution may be far from the initial one, a process of global optimization was proposed to compute the robust estimator. Furthermore, this Global Robust Estimation (GRE) scheme proved to be capable of avoiding not only gross errors but also systematic errors, and it was successfully applied to the estimation of single frequency GPS baselines affected by a strong ionospheric delay which were unsolvable by classic methods (Baselga and García-Asenjo 2008).…”

Section: Introductionmentioning

confidence: 99%

Multipath Mitigation by Global Robust Estimation

Baselga

García-Asenjo

2008

J. Navigation

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In the last decades, robust estimation has been widely applied to overcome the presence of gross errors in observations. Recently it has been shown that robust estimation, if computed appropriately, is able to cope with a larger amount of gross errors and also capable of providing reliable estimations in situations where systematic errors are present. It is the purpose of this paper to propose the use of global robust estimation for computing baselines strongly affected by multipath effects.

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“…One approach is to combine metaheuristic algorithms (MHs), to optimize the objective function. This strategy has been applied in several studies in geodesy, as it may lead to better results than classical methods [15,16,17,18,19,20,21]. In MH research, the particle swarm optimization (PSO) [22] has been widely applied, followed by the artificial bee colony [23,24,25] and ant colony optimization methods [26], more recently [27].…”

Section: Introductionmentioning

confidence: 99%

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

Koch

Klein

Gonzaga

et al. 2019

Sensors

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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.

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GNSS Differential Positioning by Robust Estimation

Cited by 24 publications

References 6 publications

Submillimetric GPS distance measurement over short baselines: noise mitigation by global robust estimation

Submillimetric GPS distance measurement over short baselines: noise mitigation by global robust estimation

Multipath Mitigation by Global Robust Estimation

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

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