This paper proposes a soft range limited K nearest neighbours (SRL-KNN)
localization fingerprinting algorithm. The conventional KNN determines the
neighbours of a user by calculating and ranking the fingerprint distance
measured at the unknown user location and the reference locations in the
database. Different from that method, SRL-KNN scales the fingerprint distance
by a range factor related to the physical distance between the user's previous
position and the reference location in the database to reduce the spatial
ambiguity in localization. Although utilizing the prior locations, SRL-KNN does
not require knowledge of the exact moving speed and direction of the user.
Moreover, to take into account of the temporal fluctuations of the received
signal strength indicator (RSSI), RSSI histogram is incorporated into the
distance calculation. Actual on-site experiments demonstrate that the new
algorithm achieves an average localization error of 0.66 m with 80% of the
errors under 0.89 m, which outperforms conventional KNN algorithms by 45%
under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
K-nearest neighbor (KNN), fingerprint-based localizatio