Journal: Acta Astronautica

Abbreviation

Acta astronaut.

Publisher

Pergamon

Journal Volumes

ISSN

0094-5765

Description

Filters

2011 - 201942020 - 20231
Reset filters

Search Results

Publications 1 - 5 of 5
  • TanDEM-X
    Item type: Journal Article
    Krieger, Gerhard; Zink, Manfred; Bachmann, Markus; et al. (2013)
    Acta Astronautica
  • Modeling of tethered satellite formations using graph theory
    Item type: Journal Article
    Larsen, Martin B.; Smith, Roy; Blanke, Mogens (2011)
    Acta Astronautica
    Tethered satellite formations have recently gained increasing attention due to future mission proposals. Several different formations have been investigated for their dynamic properties and control schemes have been suggested. Formulating the equations of motion and investigation which geometries could form stable formations in space are cumbersome when done at a case to case basis, and a common framework providing a basic model of the dynamics of tethered satellite formations can therefore be advantageous. This paper suggests the use of graph theoretical quantities to describe a tethered satellite formation and proposes a method to deduce the equations of motion for the attitude dynamics of the formation in a compact form. The use of graph theory and Lagrange mechanics together allows a broad class of formations to be described using the same framework. A method is stated for finding stationary configurations and an upper limit of their number is determined. The method is shown to be valid for general tethered satellite formations that form a tree structure.
  • Parabolic maneuvers of the Swiss Air Force fighter jet F-5E as a research platform for cell culture experiments in microgravity
    Item type: Journal Article
    Studer, Marc; Bradacs, Gesine; Hilliger, Andre; et al. (2011)
    Acta Astronautica
  • High-resolution tropospheric refractivity fields by combining machine learning and collocation methods to correct earth observation data
    Item type: Journal Article
    Shehaj, Endrit; Miotti, Luca; Geiger, Alain; et al. (2023)
    Acta Astronautica
    Signals used for Earth observation, when travelling through the atmosphere, are sensitive to refractivity; especially high spatio-temporal variations of water vapor are difficult to model and correct. Remaining unmodeled tropospheric delays deteriorate the positioning solution and therefore limit the accuracy of sensing and navigation applications. These delays are usually computed with empirical models based on ground meteorological parameters (pressure, temperature and water vapor partial pressure). However, existing models are not accurate enough for high-precision applications such as GNSS, where in consequence the so-called zenith total delay (ZTD) has to be estimated together with other unknown parameters (coordinates etc.). For decades, the Institute of Geodesy and Photogrammetry at ETH Zurich has been studying collocation methods for the modeling of tropospheric delays using meteorological parameters, successfully interpolating pointwise or integral atmospheric observations. Meanwhile, machine learning has become a widely used and valuable alternative, when big datasets are available for the training process. Indeed, we have already successfully predicted ZTDs based on meteorological parameters with an accuracy of 1–2 cm for locations (GNSS stations) already used in the training phase. However, difficulties arise to predict delays at new locations. In this work, we take a step forward in investigating the combination of machine learning algorithms and physical models used in a collocation approach to derive atmospheric delay fields at a very high resolution. Thus, without processing any GNSS data we can predict tropospheric delay fields everywhere in the area of investigation. In this paper, we firstly describe the designed architecture of the neural network, secondly, the combination of least-squares collocation and artificial neural networks for high-resolution prediction of tropospheric delays. We benefit from the complementary characteristics of these algorithms. While machine learning is capable of successfully predicting the variation of time series for given points, empirical models based on collocation are well suited for describing spatial variations within the area of investigation. Finally, we report the achieved performance for the entire territory of Switzerland (1–2 cm in terms of RMS), showing that the synergic combination of these algorithms can overcome the individual drawbacks of each method and provide more accurate delay estimates than either method individually. Datasets of 11 years, covering the territory of Switzerland, consisting of GNSS ZTDs from 72 permanent AGNES/COGEAR (swisstopo, ETHZ) stations and meteorological data from MeteoSwiss were used for this research.
  • Prediction of trabecular bone qualitative properties using scanning quantitative ultrasound
    Item type: Journal Article
    Qin, Yi-Xian; Lin, Wei; Mittra, Erik; et al. (2013)
    Acta Astronautica
Publications 1 - 5 of 5