Network Centric Warfare Communications with Wireless Sensor Networks and Data Fusion
In this thesis we aim to design efficient algorithms for wireless ad hoc sensor
networks that are supporting network-centric warfare operations. These algorithms
should conform to the hard end to end QoS requirements. They should be energy
efficient. They should fuse and aggregate data to reduce the network traffic and obtain
more accurate assessment of the environment. A particular challenge in the wireless
sensor network setting is the need for distributed estimation algorithms which balance
the limited energy resource at a node with the cost of communication and sensing.
Distributed processing strategies that use a subset of sensor measurements directly
mitigate the volume of inter-node communication thereby conserving power. The challenge
is to decide in an intelligent manner which sensor measurements to use. In other
words, to select a sensor that is likely to provide the greatest improvement to the
estimation at the lowest cost.
For target tracking applications, wireless sensor nodes provide accurate information
since they can be deployed and operated near the phenomenon. These sensing
devices have the opportunity of collaboration among themselves to improve the target
localization and tracking accuracies. An energy-efficient collaborative target tracking
paradigm is developed for wireless sensor networks (WSNs). A mutual informationbased
sensor selection (MISS) algorithm is adopted for participation in the fusion process.
MISS allows the sensor nodes with the highest mutual information about the
target state to transmit data so that the energy consumption is reduced while the
desired target position estimation accuracy is met. In addition, a novel approach to
energy savings in WSNs is devised in the information-controlled transmission power
adjustment (ICTP), where nodes with more information use higher transmission powers
than those that are less informative to share their target state information with the
neighboring nodes. Simulations demonstrate the performance gains offered by MISS
and ICTP in terms of power consumption and target localization accuracy.
A fully-distributed collaborative multi-target tracking framework that eliminates
the need for a central data associator or a central coordinating node for wireless sensor
networks is defined. Details of the distributed data association architecture, which is
more feasible than the ones relying on a coordinating entity, is described. It is shown
that for target tracking applications, the collaboration improves the target localization
performance of the distributed data collecting devices. In order to reduce the communication
energy exhausted for collaboration, the performance of the collaboration logic
manager is examined. Simulation results show that collaborating about a single target
information is a rational decision. The problem of deciding which target information
to collaborate among the detected targets arises. A mutual information based metric
is shown to be a good candidate for deciding on the target which the sensor will
collaborate about with the network.
A fuzzy network association algorithm (FUNA) for associating the target report
from the neighboring sensor node with a track in the track list is described. The rule
base of FUNA is created by consulting to the result of a voting mechanism among
the fuzzy variables to support the association decision. Euclid distance, direction
difference, and speed difference between the track report from the neighboring sensor
node and the track in the track list are the fuzzy variables that support FUNA. It
is shown by simulation that FUNA reduces the number of false network associations
for the meandering targets. Moreover, better target localization accuracies achieved
by FUNA when compared to the Euclid, likelihood, and Mahalanobis distance based
network association metrics.