Method of restoring the geofield values based on data from a highly mobile geosensors network using an automatic adaptive technique for determining the parameters of the local regression kernel

Geographic Information Systems
UDC: 
52.08:004.62:681.518.3
DOI: 
10.22389/0016-7126-2021-978-12-23-33
1 Gvozdev O.G.
2 Maiorov A.A.
3 Materuhin A.V.
Year: 
2021
№: 
12
978
Pages: 
23-33

Moscow State University of Geodesy and Cartography (MIIGAiK)

1, 
2, 
3, 
Abstract:
The article deals with the matter of restoring the geofield based high-intensity spatio-temporal data streams received from a highly mobile geosensors network in real time. It includes dozen thousands of active measuring devices that measure at a frequency of up to 5 Hz and are installed on mobile platforms moving at speeds of up to 80–100 km/h. For solving this task, we propose to apply the method of local smoothing. To adapt a kernel of local regression to the conditions of a specific task, such as the features of the terrain or the simulated geofield, as well as the characteristics of the geosensor network, the authors suggested using the kernel of local regression obtained through using stochastic optimization methods. The application of genetic algorithms and the method of simulating annealing for this purpose are considered. The structure of a generalized method based on this idea is presented in the article. The results of model experiments aimed to confirm the operability of this method and those of evaluating the performance of this method in single-threaded mode are given.
The results were obtained as a part of the state task of the Ministry of Science and Higher Education of the Russian Federation (No. 0708-2020-0001).
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Citation:
Gvozdev O.G., 
Maiorov A.A., 
Materuhin A.V., 
(2021) Method of restoring the geofield values based on data from a highly mobile geosensors network using an automatic adaptive technique for determining the parameters of the local regression kernel. Geodesy and cartography = Geodezia i Kartografia, 82(12), pp. 23-33. (In Russian). DOI: 10.22389/0016-7126-2021-978-12-23-33
Publication History
Received: 12.11.2021
Accepted: 07.12.2021
Published: 31.12.2021

Content

2021 December DOI:
10.22389/0016-7126-2021-978-12