Communications and Networking. 12th International Conference, ChinaCom 2017, Xi’an, China, October 10-12, 2017, Proceedings, Part II

Research Article

A Positioning Method Based on RSSI and Power Spectrum Waveform Distinction

  • @INPROCEEDINGS{10.1007/978-3-319-78139-6_50,
        author={Yuyang Lin and Zunwen He and Jiang Yu and Yan Zhang},
        title={A Positioning Method Based on RSSI and Power Spectrum Waveform Distinction},
        proceedings={Communications and Networking. 12th International Conference, ChinaCom 2017, Xi’an, China, October 10-12, 2017, Proceedings, Part II},
        proceedings_a={CHINACOM},
        year={2018},
        month={4},
        keywords={Positioning RSSI PSWD KLD},
        doi={10.1007/978-3-319-78139-6_50}
    }
    
  • Yuyang Lin
    Zunwen He
    Jiang Yu
    Yan Zhang
    Year: 2018
    A Positioning Method Based on RSSI and Power Spectrum Waveform Distinction
    CHINACOM
    Springer
    DOI: 10.1007/978-3-319-78139-6_50
Yuyang Lin1,*, Zunwen He1,*, Jiang Yu1,*, Yan Zhang1,*
  • 1: Beijing Institute of Technology
*Contact email: ginieu@bit.edu.com, hezunwen@bit.edu.cn, jiangy@bit.edu.com, zhangy@bit.edu.com

Abstract

In this paper, we propose a positioning method based on the dual-complex fingerprint, which consists of the Received Signal Strength Indication (RSSI) and the Power Spectrum Waveform (PSW), including three stages. First, generate fingerprint library by data collected offline. For each reference point, RSSI and PSW are both stored in the library. Then make pre-positioning by RSSI fingerprint and the location of reference points. These points will be selected twice to remove the single points away from the others. Final positions are estimated by taking PSW Distinction (PSWD) and RSSI into consideration. In addition, we introduce an idea of evaluating PSWD by the Kullback-Leibler Distance (KLD). The MATLAB simulation results show that, comparing to other algorithms such as KNN and WKNN, the proposed method leads to lower number of observable misestimated points, and approximately 5% improvement in cumulative distribution function (CDF) of position error within 1.3 m.