7th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

Location estimation of radio transmitters based on spatial interpolation of RSS values

Download676 downloads
  • @INPROCEEDINGS{10.4108/icst.crowncom.2012.248454,
        author={Valentin Rakovic and Marko Angjelicinoski and Vladimir Atanasovski and Liljana Gavrilovska},
        title={Location estimation of radio transmitters based on spatial interpolation of RSS values},
        proceedings={7th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2012},
        month={7},
        keywords={radio intereference field localization cognitive radio interpolation},
        doi={10.4108/icst.crowncom.2012.248454}
    }
    
  • Valentin Rakovic
    Marko Angjelicinoski
    Vladimir Atanasovski
    Liljana Gavrilovska
    Year: 2012
    Location estimation of radio transmitters based on spatial interpolation of RSS values
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2012.248454
Valentin Rakovic1, Marko Angjelicinoski1, Vladimir Atanasovski1, Liljana Gavrilovska1,*
  • 1: Faculty of Electrical Engineering and Information Technologies Ss Cyril and Methodius University in Skopje
*Contact email: liljana@feit.ukim.edu.mk

Abstract

Detection of potential transmitters' location is one of the vital aspects for efficient practical deployment of secondary spectrum access solutions. It requires accurate and up-to-date radio environmental estimation. Spatial interpolation techniques can allow partial or complete insight into the radio field, the interference and the possible geo-locations of various field transmitters depending on the number of radio measurements performed in sparse locations. This paper presents an effective solution based on spatially interpolated Received Signal Strength (RSS) values for location estimation of radio transmitters, which operates on Radio Interference Field (RIF) maps obtained by interpolating measurement data from N sparsely distributed sensors. In contrast to the known range based localization methods the developed technique also achieves higher computational efficiency. The performance analysis shows that the proposed method is suitable for both outdoor and indoor environments and is capable of reliable detection of multiple sources even for low number of sensors.