Emerging New Trends in Hybrid Vehicle Localization Systems - Global Navigation Satellite Systems: Signal,Theory and Applications - page 288

the BS's changes when BS's are removed or new ones are added. Nevertheless, this method is
becoming more attractive for indoor applications because the database creation can be more
comprehensive and manageable.
4.2 VANET localization using relative distances measurements
This approach takes advantage of the emerging VANET environments. The distances between
VANET nodes are estimated and exchanged among vehicles along with preliminary estimates
of the vehicles' locations. Vehicles can then use this information to construct local relative
position maps that contain the vehicles and their neighbours. This strategy has initially
emerged in Wireless Sensor Networks (WSN), but recently, a number of solutions have been
proposed for use in VANET Benslimane (2005); Drawil & Basir (2008); Parker & Valaee (2007)
4.2.1 Vehicle localization in VANET
A VANET based localization method was introduced in Benslimane (2005) for localizing
vehicles with no GPS receivers, or those whose location can not be determined because
satellite signals have been lost, for instance, in a tunnel. With this method, vehicles that are
not equipped with GPS determine their own locations by relying on information they receive
from vehicles that are equipped with GPS. Vehicles within transmission range can measure the
distances between each other using one of the radio-location methods presented in Caffery &
Stuber (1998). By finding its closest three neighbours the unequipped vehicle can compute its
position using trilateration.
4.2.2 Cooperative vehicle position estimation
The work reported in Parker & Valaee (2006) presents a method of distributed vehicle
localization in VANET. The method utilizes RSS measurements to estimate the distances
between one vehicle and others in its coverage area. It is assumed that vehicles initially
estimate their own locations using a GPS receiver and then exchange their location
information so that they can perform an optimization technique in order to improve their
location estimates.
This technique demonstrates robustness of location estimates. However, it lacks the ability to
detect and avoid the effect of multipath signals in the GPS measurements, which drastically
degrades the localization accuracy in multipath environments (e.g., urban canyons).
In Drawil & Basir (2010) an algorithm called InterVehicle-Communication-Assisted
Localization (IVCAL) is proposed to mitigate the multipath effect in the location estimates
of vehicles in VANET. A KF and an inter-vehicle-communication system collaborate in order
to increase the robustness and accuracy of the localization of every vehicle in the network.
The two main components that allow the inter-vehicle-communication system and the KF to
interact are the Multipath Detection Unit (MDU), which detects the existence of a multipath
effect in the output of the KF, and the Localization Enhancement Unit (LEU), which obtains
the neighbours' information from the inter-vehicle-communication system and feeds an
optimized location estimate back to the KF (Figure 4). As in Jabbour, Cherfaoui & Bonnifait
(2006) and Jabbour, Bonnifait & Cherfaoui (2006), KF innovation is used as an indication of the
contamination of the GPS measurement, and it has therefore been used as a learning pattern
for the MDU in IVCAL. An uncertainty measure is also utilized in order to specify a subset
of the most accurate network neighbours that can be used as anchors to enable vehicles to
improve their location estimates.