The goal of this thesis is the development, implementation and
assessment of efficient particle filters (PFs) for various target tracking
applications on wireless sensor networks (WSNs).
We first focus on developing efficient models and particle filters for
indoor tracking using received signal strength (RSS) in WSNs. RSS is
a very appealing type of measurement for indoor tracking because of its
availability on many existing communication networks. In particular, most
current wireless communication networks (WiFi, ZigBee or even cellular
networks) provide radio signal strength (RSS) measurements for each radio
transmission. Unfortunately, RSS in indoor scenarios is highly influenced
by multipath propagation and, thus, it turns out very hard to adequately
model the correspondence between the received power and the transmitterto-
receiver distance. Further, the trajectories that the targets perform in
indoor scenarios usually have abrupt changes that result from avoiding walls
and furniture and consequently the target dynamics is also difficult to model.
In Chapter 3 we propose a flexible probabilistic scheme that allows
the description of different classes of target dynamics and propagation
environments through the use of multiple switching models. The resulting
state-space structure is termed a generalized switching multiple model
(GSMM) system. The drawback of the GSMM system is the increase in the
dimension of the system state and...