Network-centric warfare communications with wireless sensor networks and data fusion - Önel, Tolga. (Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2009., 2009)
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 information- based 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 met- ric 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.
Keywords: Wireless sensor networks.; Multisensor data fusion.