IEEE Systems, Man and Cybernetics Magazine - October 2020 - 24
synchronize devices, and conduct
daily life. However, GNSS is susceptible to jamming and spoofing and is
often unavailable in places without a
clear sky, such as urban canyons
or indoors.
INSs offer a navigation system
that is self contained after initialization. However, consumer-grade
INSs are susceptible to drift and
require frequent correction based
off of known positions from GNSS.
Various approaches have been
developed over the years, including foot-mounted devices [4], using
Without measurement
points inside of the
field of inference,
the model relies on
relaxed requirements
for channel flicker at
the interior edge of
boundaries.
BSSID
RSSI
GPS
"Truth Position"
prb
rIbI
RSSI
Point Map
particle filtering algorithms and
leveraging Wi-Fi signals [5], or
using magnetic compasses [6].
Additional approaches that utilize
probabilistic models have also
been tested, but these methods
usually focus on a range gate from
access points (APs) of known origin [7], [8]. All of these approaches
suffer from large overhead, lack of
fast convergence, or the need for
extensive calibration. In [9], an
approach using an indoor localization approach is presented that is
based on accurate fingerprinting
IMU
Inertial
Navigation
Processor
Integrated
Navigation
Solution
Model
Generation
Model
Validity
Check
Kalman
Filter
RSSI
Positioning
Figure 1. The upper-level diagram of RSSI location finding integrated with an INS. The data paths implemented
in this research are shown as solid lines, and the data paths that have yet to be implemented are depicted
as dotted lines. Note that the RSSI positioning as proposed in this article would be implemented as a position
update. IMU: inertial measurement unit.
24
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE O ctober 2020
IEEE Systems, Man and Cybernetics Magazine - October 2020
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