In this paper, the problem of mobile tracking in
dense environments is studied. The Global Positioning System
(GPS) is the most accessible positioning technique. However, GPS
does not work properly in indoor and dense areas, as the receiver
typically does not have access to a sufficient number of line-ofsight
satellites. Therefore, localization in these networks can be
alternatively done by using measurements collected within the
network and without the aid of any external resources (e.g., GPS).
The mobile tracking problem includes several static reference
nodes whose locations are fixed and known, and many mobile
nodes whose locations are unknown and needed to be determined.
The problem of mobile tracking can be solved in two forms:
centralized and distributed. A centralized algorithm can result in
high complexity and latency, while a distributed algorithm might
lead to large estimation errors. In this paper, a novel cooperative
localization technique is introduced which is able to deliver a
promising localization accuracy while maintain the latency and
complexity as low as possible. The performance of the proposed
algorithm is compared with those of other algorithms in terms of
localization accuracy, latency, and required data communication
through computer simulations. The simulation results show the
effectiveness of the proposed algorithm in comparison with either
centralized and distributed algorithms. An important application
of this work is vehicle localization in dense environments where
the vehicles do not have access to GPS satellites and must be
localized by the elements within the network.