Bojan Stopar

Spatio-temporal evaluation matrices for geospatial data

Abstract The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.

Wavelet Neural Network employment for continuous GNSS orbit function construction: Application for the Assisted-GNSS principle

This paper presents a Wavelet Neural Network (WNN) employment for discrete precise ephemerides tabular data of Global Navigation Satellite System (GNSS) orbit approximation to obtain continuous orbit function. Orbit function is essential in positioning and navigation tasks, the advantage of continuity, however, is that it can also be used during GNSS signal interruptions. The essence of WNN continuous orbit construction is single function determination for the entire interval, while the interpolation methods follow several discrete function establishment. Specifically, we investigate the performance of the WNN continuous orbit approximation by comparison with well known polynomial and trigonometric interpolations. The experimental results show that our proposed method is superior to the traditional methods especially near the end of intervals, because they are not subject to large scale function oscillations as in the case of polynomials constructions. We propose a WNN construction using different mother functions of the WNN namely Mexican hat, Morlet function, Gaussian and Daubechies (D4) wavelet. Furthermore best algorithm for regression estimation is described; selection of neurons in the hidden layer of WNN is based on orthogonal least squares algorithm. The main objective of this article is to show that the presented method of orbit function construction could be used for GNSS ephemerides distribution and short-time prediction in the Assisted GNSS-networks.

GPS orbit approximation using radial basis function networks

We present solutions for GPS orbit computation from broadcast and precise ephemerides using a group of artificial neural networks (ANNs), i.e. radial basis function networks (RBFNs). The problem of broadcast orbit correction, resulting from precise ephemerides, has already been solved using traditional polynomial and trigonometric interpolation. As an alternative approach RBFN broadcast orbit correction produces results within the accuracy range of the traditional methods. Our study shows RBFN broadcast orbit correction performs well also near the end of data intervals and for short data spans (~20min). Regarding limitations of polynomial and trigonometric extrapolation, the most significant advantage of using RBFNs over the traditional methods for GPS broadcast orbit approximation arises from its short time prediction capability.

The use of GPS in the fourth EPOCH measurements of crustal movements along the Ljubljana fault

Abstract Three triangulation and trilateration micro networks in the seismic region of Ljubljana city, for the determination of horizontal and vertical movements, have been measured for the fourth time. GPS measurements have been included into the networks for the first time. The problem of the distance measurements was solved with the precise distancemeter Kern Mekometer ME 5000, angle measurements have been done with the electronic theodolite Kern E2. The problem of the orientation of the separated local networks was solved with GPS measurements. The results show that displacement in all three nets are not in the direction of the local fault but in the direction of the Sava fault and at the same time in the direction of the edge of the Peradriatic plate.