04416nam 2200961z- 450 991055715400332120231214133508.0(CKB)5400000000040517(oapen)https://directory.doabooks.org/handle/20.500.12854/68374(EXLCZ)99540000000004051720202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStochastic Models for Geodesy and Geoinformation ScienceBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (200 p.)3-03943-981-2 3-03943-982-0 In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.History of engineering & technologybicsscEM-algorithmmulti-GNSSPPPprocess noiseobservation covariance matrixextended Kalman filtermachine learningGNSS phase biassequential quasi-Monte Carlovariance reductionautoregressive processesARMA-processcolored noisecontinuous processcovariance functionstochastic modelingtime serieselementary error modelterrestrial laser scanningvariance-covariance matrixterrestrial laser scannerstochastic modelB-spline approximationHurst exponentfractional Gaussian noisegeneralized Hurst estimatorvery long baseline interferometrysensitivityinternal reliabilityrobustnessCONT14Errors-In-Variables ModelTotal Least-Squaresprior informationcollocation vs. adjustmentmean shift modelvariance inflation modeloutlierdetectionlikelihood ratio testMonte Carlo integrationdata snoopingGUM analysisgeodetic network adjustmentstochastic propertiesrandom number generatorMonte Carlo simulation3D straight line fittingtotal least squares (TLS)weighted total least squares (WTLS)nonlinear least squares adjustmentdirect solutionsingular dispersion matrixlaser scanning dataHistory of engineering & technologyNeitzel Frankedt1303359Neitzel FrankothBOOK9910557154003321Stochastic Models for Geodesy and Geoinformation Science3026943UNINA