Talks and Poster Presentations (with Proceedings-Entry):

B. Dong, T. Burgess, S. Fercher, H.-B. Neuner:
"Neural Network Based Radio Fingerprint Similarity Measure";
Talk: 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes (invited); 2018-09-24 - 2018-09-27; in: "IPIN 2018", (2018), 8 pages.

English abstract:
The radio signal Received Signal Strength Indicator
(RSSI) based localization method is one of the most often adopted
indoors localization methods, due to the fact that it can be used
by any handhold devices on the market without any modification.
This paper will present a new deep learning inspired model to
predict locational distance/similarity between two points based
on their RSSI measurement. This model uses carefully designed
input features, neural network architecture, as well as purposely
crafted pre-training to combine the features and statistics from
multiple hand crafted RSSI to locational similarity models (reference
models). The reference models include a RSSI difference
based model (euclidean distance), number of visible signals by
both measurements (Jaccard distance) and the rank difference
of commonly visible signals in the comparing measurements
(Spearmanīs footrule). Our evaluation shows the three reference
models have very distinct strengths and weaknesses: the euclidean
distance based model generates the most detailed prediction and
has best estimation results when the two measurement points are
close to each other; the Jaccard distance based model can only
provide a very coarse estimation, however, it can distinct points
that are far away; the Spearmanīs footrule based solution has
overall good but coarse estimation, and its estimations represent
the relative distance very well, especially in the middle range.
The proposed method, as we expected, combines the best features
from all three reference models and generates the best locational
distance estimation.

Indoor navigation, RSSI, footrule, localization, deep learning, neural network, bluetooth, calibration

Created from the Publication Database of the Vienna University of Technology.