Abstract: Flight management system (FMS) is a specialized
computer system that automates a wide variety of in-flight tasks,
reducing the workload on the flight crew to the point that modern
aircraft no longer carry flight engineers or navigators. The primary
function of FMS is to perform the in-flight management of the flight
plan using various sensors (such as GPS and INS often backed up by
radio navigation) to determine the aircraft's position. From the
cockpit FMS is normally controlled through a Control Display Unit
(CDU) which incorporates a small screen and keyboard or touch
screen. This paper investigates the performance of GPS/ INS
integration techniques in which the data fusion process is done using
Kalman filtering. This will include the importance of sensors
calibration as well as the alignment of the strap down inertial
navigation system. The limitations of the inertial navigation systems
are investigated in order to understand why INS sometimes is
integrated with other navigation aids and not just operating in standalone
mode. Finally, both the loosely coupled and tightly coupled
configurations are analyzed for several types of situations and
operational conditions.
Abstract: Terminal localization for indoor Wireless Local Area
Networks (WLANs) is critical for the deployment of location-aware
computing inside of buildings. A major challenge is obtaining high
localization accuracy in presence of fluctuations of the received signal
strength (RSS) measurements caused by multipath fading. This paper
focuses on reducing the effect of the distance-varying noise by spatial
filtering of the measured RSS. Two different survey point geometries
are tested with the noise reduction technique: survey points arranged
in sets of clusters and survey points uniformly distributed over the
network area. The results show that the location accuracy improves
by 16% when the filter is used and by 18% when the filter is applied
to a clustered survey set as opposed to a straight-line survey set.
The estimated locations are within 2 m of the true location, which
indicates that clustering the survey points provides better localization
accuracy due to superior noise removal.