R. U. R. Lighari, M. Berg, E. T. Salonen and A. Parssinen, "Classification of GNSS SNR data for different environments and satellite orbital information," 2017 11th European Conference on Antennas and Propagation (EUCAP), Paris, 2017, pp. 2088-2092. doi: 10.23919/EuCAP.2017.7928672
Classification of GNSS SNR data for different environments and satellite orbital information
|Author:||Rahman Lighari, Rameez UR1; Berg, Markus1; Salonen, Erkki T.1;|
1Centre for Wireless Communications – Radio Technology Research Unit, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018073133174
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-07-31
In this paper, a data classification method for analyzing the aspects of Signal-to-Noise Ratio (SNR) for Global Navigation Satellite System (GNSS) in real conditions is introduced. Different parts of measured environments and the orbital information of satellites are used as criteria for data classification. It consists of: 1) taking fish eye images of measured routes; 2) dividing measured environments into four potential sub environments (open area, forest area, single building blockage, and street canyon); 3) classifying satellites into nine different groups as function of elevation angles; and 4) creating a table containing the information of mean and standard deviation of SNR for different environments and satellite elevation angles. Results show good correlation of SNR's between same sub environments for different satellite elevation ranges which offer useful insight to regenerate a generalized set of SNR parameters in the laboratory environment for the development of 3D GNSS channel model.
|Pages:||2088 - 2092|
2017 11th European Conference on Antennas and Propagation (EUCAP)
European Conference on Antennas and Propagation
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research is supported by the Finnish Funding Agency for Innovation (Tekes) through the Hilla research program and Centre for Wireless Communications.
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