Journal
IEEE SENSORS JOURNAL
Volume 19, Issue 19, Pages 8940-8950Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2019.2922772
Keywords
Device-free localization; radio tomographic imaging; indoor positioning systems; spring relaxation; histogram distance
Funding
- Massey University Doctoral Scholarships
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Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the long-term and short-term histograms. The existing HD-DFL methods use either Kullback-Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.
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