GNSS is widely used for outdoor location and navigation.
GPS was successfully augmented by GLONASS. In
future the constellation of navigation satellites will be
enhanced by Galileo, Compass, QZSS. Large satellite
constellation, improvement of ephemeris quality, and
using triple frequency band pseudo-range and carrier
phase measurements will potentially make robust
standalone solution to converge to the sub decimeter level
of accuracy for most part of outdoor navigation
applications. Assisted GNSS expands its capabilities to
harsh conditions, but nevertheless GNSS is blind in deep
indoor environments. So other approaches to positioning
have been proposed for indoor navigation. One of most
widely used approaches is based on using 802.11 Wi-Fi.
It allows for obtaining of low cost location solution using
already deployed access points (AP) just modifying
firmware part of the system. Received signal strength
indicator (RSSI) is widely used in navigation equations as
raw measurements the same way as pseudo-ranges are
used in satellites navigation equations. But, classic
logarithmic loss function connecting distance to AP with
RSSI readings can be used only in ideal environments,
free of sources of fading and multipath. Understanding of
this disadvantage gave rise of using fingerprint-based
methods, connecting samples (or fingerprints) of RSSI
measurements with certain calibration (or anchor) points
whose precise position is stored in the calibration
database together with RSSI fingerprint vectors.
Comparing measured RSSI values from plurality of AP’s
with stored samples of fingerprints gives a key for indoor
position determination.