Demet Cilden-Guler | Searcharxiv

arXiv2026

Geo-Standardizing 3D Modeling of Surface/Subsurface Objects and Related Logical Spaces on Celestial Bodies: Case Studies for Moon and Mars

Establishing frameworks for promoting the realization of various activities on celestial bodies sustainably is of great significance for different contexts, such as preserving the scientific evidence and space heritage. Therefore, this research first proposes a conceptual model that covers the different types of features, attributes, and relationships between them to comprehensively delineate the surface/subsurface objects and related logical spaces on celestial bodies. It then implements this conceptual model as a CityJSON extension in such a way that allows for creating the three-dimensional (3D) geodatasets that represent these objects and spaces in a standardized manner. Moreover, the usefulness of this study is demonstrated through creating CityJSON datasets that include 3D models of exemplary surface/subsurface objects from the Moon and Mars, such as a historical landing site and related logical spaces, such as exclusion zones for protecting this site. The results of the current study show that there is a strong potential for forming 3D geodatasets on celestial bodies that can provide a notable foundation for the technical implementation of international agreements and legal frameworks. This work also contributes to the design of planetary spatial data infrastructures (PSDI) by incorporating the third dimension.

cs.CY↗

arXiv2021

Kalman filtering with empirical noise models

Most Kalman filter extensions assume Gaussian noise and when the noise is non-Gaussian, usually other types of filters are used. These filters, such as particle filter variants, are computationally more demanding than Kalman type filters. In this paper, we present an algorithm for building models and using them with a Kalman type filter when there is empirically measured data of the measurement errors. The paper evaluates the proposed algorithm in three examples. The first example uses simulated Student-t distributed measurement errors and the proposed algorithm is compared with algorithms designed specifically for Student-t distribution. Last two examples use real measured errors, one with real data from an Ultra Wideband (UWB) ranging system, and the other using low-Earth orbiting satellite magnetometer measurements. The results show that the proposed algorithm is more accurate than algorithms that use Gaussian assumptions and has similar accuracy to algorithms that are specifically designed for a certain probability distribution.

stat.AP↗