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Advisor(s)
Abstract(s)
Fingerprinting is an indoor location technique,
based on wireless networks, where data stored during the offline
phase is compared with data collected by the mobile device
during the online phase. In most of the real-life scenarios, the
mobile node used throughout the offline phase is different from
the mobile nodes that will be used during the online phase.
This means that there might be very significant differences
between the Received Signal Strength values acquired by
the mobile node and the ones stored in the Fingerprinting
Map. As a consequence, this difference between RSS values
might contribute to increase the location estimation error. One
possible solution to minimize these differences is to adapt the
RSS values, acquired during the online phase, before sending
them to the Location Estimation Algorithm. Also the internal
parameters of the Location Estimation Algorithms, for example
the weights of the Weighted k-Nearest Neighbour, might need
to be tuned for every type of terminal. This paper focuses
both approaches, using Direct Search optimization methods to
adapt the Received Signal Strength and to tune the Location
Estimation Algorithm parameters. As a result it was possible
to decrease the location estimation error originally obtained
without any calibration procedure.
Description
Keywords
Fingerprinting location IEEE 802.11 Direct search optimization methods LEA calibration RSS adaptation
Citation
Publisher
International Association of Engineers