LEADER 04435nam 2200973z- 450 001 9910557154003321 005 20210501 035 $a(CKB)5400000000040517 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68374 035 $a(oapen)doab68374 035 $a(EXLCZ)995400000000040517 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aStochastic Models for Geodesy and Geoinformation Science 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (200 p.) 311 08$a3-03943-981-2 311 08$a3-03943-982-0 330 $aIn 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. 606 $aHistory of engineering and technology$2bicssc 610 $a3D straight line fitting 610 $aARMA-process 610 $aautoregressive processes 610 $aB-spline approximation 610 $acollocation vs. adjustment 610 $acolored noise 610 $aCONT14 610 $acontinuous process 610 $acovariance function 610 $adata snooping 610 $adirect solution 610 $aelementary error model 610 $aEM-algorithm 610 $aErrors-In-Variables Model 610 $aextended Kalman filter 610 $afractional Gaussian noise 610 $ageneralized Hurst estimator 610 $ageodetic network adjustment 610 $aGNSS phase bias 610 $aGUM analysis 610 $aHurst exponent 610 $ainternal reliability 610 $alaser scanning data 610 $alikelihood ratio test 610 $amachine learning 610 $amean shift model 610 $aMonte Carlo integration 610 $aMonte Carlo simulation 610 $amulti-GNSS 610 $anonlinear least squares adjustment 610 $aobservation covariance matrix 610 $aoutlierdetection 610 $aPPP 610 $aprior information 610 $aprocess noise 610 $arandom number generator 610 $arobustness 610 $asensitivity 610 $asequential quasi-Monte Carlo 610 $asingular dispersion matrix 610 $astochastic model 610 $astochastic modeling 610 $astochastic properties 610 $aterrestrial laser scanner 610 $aterrestrial laser scanning 610 $atime series 610 $atotal least squares (TLS) 610 $aTotal Least-Squares 610 $avariance inflation model 610 $avariance reduction 610 $avariance-covariance matrix 610 $avery long baseline interferometry 610 $aweighted total least squares (WTLS) 615 7$aHistory of engineering and technology 700 $aNeitzel$b Frank$4edt$01303359 702 $aNeitzel$b Frank$4oth 906 $aBOOK 912 $a9910557154003321 996 $aStochastic Models for Geodesy and Geoinformation Science$93026943 997 $aUNINA