A strategy for the analysis of the stability of reference points in deformation surveys

Chen, Y.Q.; Chrzanowski, Adam; Secord, James M.

doi:10.1139/geomat-1990-0016

Cited by 56 publications

(44 citation statements)

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“…After determination of the vector of displacement for all analyzed points in the control network, a common estimator of global variance factor for two measurement epochs is calculated e. g. (Chen, 1983;Chen et al, 1990;Caspary, 2000;Setan and Singh, 2001;Prószyński and Kwaśniak, 2006). In the theoretical model, which is the linearized form of the initial nonlinear relationships is the covariance matrix of observations, n is a number of observations and u is number of unknowns, a common estimator of global variance factor for two measurement epochs is calculated on the basis of the formula…”

Section: Classical Evaluation Of the Signifi Cance Of Displacement Vementioning

confidence: 99%

“…This problem is well-known. More information can be found in papers: e. g. (Prószyński, 1986;Chen et al, 1990;Prószyński and Kwaśniak, 2006).…”

Section: Classical Evaluation Of the Signifi Cance Of Displacement Vementioning

confidence: 99%

“…The classical evaluation of the signifi cance of displacements for individual points in such networks is based on the a priori weight matrix of observations and the estimator of global variance factor e. g. (Chen, 1983;Chen et al, 1990;Caspary, 2000;Setan and Singh, 2001;Prószyński and Kwaśniak, 2006). This estimator is common for all adjusted observations and is, unfortunately, not very effective in the evaluation of weighting for heterogeneous measurement results.…”

Section: Introductionmentioning

confidence: 99%

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Statistical significance of displacements in heterogeneous control networks

Nowel¹,

Kamiński²

2013

Geodesy and Cartography

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This paper proposes a modifi cation of the classical process for evaluating the statistical signifi cance of displacements in the case of heterogeneous (e.g. linear-angular) control networks established to deformation measurements and analysis. The basis for the proposed solution is the idea of local variance factors. The theoretical discussion was complemented with an example of its application on a simulated horizontal control network. The obtained results showed that the evaluation of the statistical signifi cance of displacements in the case of heterogeneous control networks should be carried out using estimators of local variance factors.

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Section: Classical Evaluation Of the Signifi Cance Of Displacement Vementioning

confidence: 99%

“…This problem is well-known. More information can be found in papers: e. g. (Prószyński, 1986;Chen et al, 1990;Prószyński and Kwaśniak, 2006).…”

Section: Classical Evaluation Of the Signifi Cance Of Displacement Vementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical significance of displacements in heterogeneous control networks

Nowel¹,

Kamiński²

2013

Geodesy and Cartography

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“…The single point test is applied, in both cases after and before S-transformation, for each point at the significance level˛D 0:05 (see [2,4]). …”

Section: Statistical Test and Deformation Detectionmentioning

confidence: 99%

“…In order to illustrate the OKF, 2-D total station observations comprising distances and angles are used. For detecting the deformation, the ordinary single point test is applied as a local test [2,4]. For every point, the test value (f ) is computed and compared with the F-distribution table value.…”

Section: Introductionmentioning

confidence: 99%

Optimized Kalman filter versus rigorous method in deformation analysis

Aharizad

Setan

Lim

2012

Journal of Applied Geodesy

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Kalman filtering is a multiple-input, multipleoutput filter that can optimally estimate the states of a system, and applicable for deformation analysis. The states are all the variables needed to completely describe the system behavior of the deformation process as a function of time (such as position, velocity etc.). The standard Kalman filter estimates the state vector where the measuring process is described by a linear system. In order to process a non-linear system an optimized aspect of Kalman filter is required. The main purpose of this research is to evaluate the optimized Kalman filter (OKF) as a non-robust method versus the iterative weighted similarity transformation (IWST) as a rigorous (also called robust) method. To satisfy this objective, first a detailed description on executing the optimized Kalman filter using the observation of angles and distances directly is provided. Then, 2-D total station data observations comprising distances and angles are used to demonstrate the OKF. For detecting the deformation, a real point-related test (single point test) is applied for every point as a local test. Consequently, the findings from OKF are compared and evaluated against the results from the IWST method. In general, the outcome of the Kalman filter algorithm is close to the preliminary results from the IWST method. The maximum and minimum differences in computed displacements are 0.2 and 2 millimeters respectively. Finally, Kalman filter approaches, having some properties, are recognized as suitable techniques for deformation analysis.

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