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Rozvoj družicové techniky v oblasti přesného určování polohy a dostupnost mobilních zařízení s vestavěným přijímačem multi-GNSS (Global Navigation Satellite System) nám umožňuje určovat polohy s větší přesností než před několika lety. Zároveň ze strany uživatelů roste poptávka po stále přesnějším určování polohy při snižování nákladů na finální zařízení (např. navigační systém pro autonomní sekačky na trávu). Tato práce představuje koncept řešení, které integruje různé techniky určování polohy: gyroskopy, počítadla kilometrů a GNSS pomocí volně vázaného Kalmanova filtru. Model integrace senzoru byl vyvinut pro horizontální komponenty (2D) se současným určením azimutu a přesné polohy. Integrační filtr navíc využívá dynamickou matici vah, jejíž hodnoty se volí v závislosti na typu řešení GNSS (FIX, FLOAT atd.). Zkoušky měřící plošiny byly prováděny za ideálních podmínek a v místech zastřeného horizontu (průjezdy podél vysoké zdi, pod prolamovanými ocelovými vazníky apod.). Přesnosti získané během testů pomocí EKF a dynamické hmotnostní matice jsou RMS 0,019 m pro severní a východní komponenty. Přesnost určení azimutu však byla 0,59°. Prezentace navíc představí koncept platformy, která integruje levné senzory v reálném čase. Tuto práci podpořila Wroclaw University of Environmental and Life Sciences (Polsko) v rámci projektu „POMOST“ (grant č. N110/0002/22)., The development of satellite techniques in the field of precise positioning and the availability of mobile devices with a built-in multi-GNSS (Global Navigation Satellite System) receiver allow us to determine positions with better accuracy than a dozen or so years ago. At the same time, there is a growing demand from users for more and more accurate positioning while reducing the costs of the final device (e.g., a navigation system for autonomous lawn mowers). This work presents the concept of a solution that integrates different positioning techniques: gyroscopes, odometers, and GNSS using a loosely coupled Kalman filter. The sensor integration model was developed for horizontal components (2D) with simultaneous determination of azimuth and precise position. Additionally, the integration filter uses a dynamic weight matrix, the values of which are selected depending on the type of GNSS solution (FIX, FLOAT, etc.). Tests of the measurement platform were carried out under ideal conditions and in places where the horizon is obscured (passes along a high wall, under openwork steel trusses, etc.). The accuracies obtained during tests using EKF and the dynamic weight matrix are RMS 0.019 m for the North and East components. However, the accuracy of the azimuth determination was 0.59°. In addition, the presentation will present a platform concept that integrates low-cost sensors in real time. This work was supported by Wroclaw University of Environmental and Life Sciences (Poland) under the project “POMOST” (grant no. N110/0002/22).

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition., Mathematical Geodesy and Positioning

The widespread availability of GPS-enabled mobile devices has contributed towards an unprecedented volume of data on human movement. Human mobility data are the key input for developing accurate mobility models that can support decision-making in, for example, urban planning, transportation planning and disease spread. However, the increasing geoprivacy concerns have been limiting the use of and access to such data. For this reason, the WHO-WHERE-WHEN (3W) model, a privacy-protective model for generating synthetic mobility data, has been developed. However, human mobility is affected by multiple factors that must be accounted for to produce synthetic mobility trajectories that accurately simulate the fluctuations of population in a study area. The 3W model already considers four main factors affecting human mobility: size and shape of activity spaces, circadian rhythm, and home and work locations. Yet, meteorological factors are known to affect human mobility patterns but, to our knowledge, there is not a model that accounts for weather conditions. In this paper, we propose a theoretical framework to extend the 3W model to a 4W model: WHO-WHERE-WHEN-WEATHER. We hypothesise that accounting for weather conditions in human mobility predictions will increase the overall accuracy of predicted mobility patterns.

Tropospheric wet delay, the main source of which is water vapor, is one of the major factors affecting the accuracy of positioning techniques using microwave. Tropospheric tomography is a powerful method to reconstruct the water vapor content in four-dimensional (4D) space. This paper studies the effect of using function-based and voxel-based tropospheric tomography methods on the positioning accuracy. This examination is performed on the static and kinematic positioning modes using Global Navigation Satellite Systems (GNSS) stations under different weather conditions. After validating the results of tomography methods using radiosonde observations, the tomography-based positioning solutions, including function-based and voxel-based approaches, are compared with the positions obtained using tropospheric models. The results of two GPS stations show that the accuracy increases when applying tomography approaches. The function-based tomography is able to increase the accuracy of the up component of the static and kinematic modes by about 0.42 and 0.79 cm, respectively, compared to the voxel-based method. In addition, the use of the function-based tropospheric tomography can decrease the convergence time of the kinematic Precise Point Positioning (PPP) solution., Mathematical Geodesy and Positioning

Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for diferently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We fnd that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.