INFORMATION-THEORETIC OPTIMIZATION OF WIRELESS SENSOR NETWORKS AND RADAR SYSTEMS
Wireless sensor network
AdvisorGoodman, Nathan A.
Committee ChairGoodman, Nathan A.
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThree information measures are discussed and used as objective functions for optimization of wireless sensor networks (WSNs) and radar systems. In addition, a long-term system performance measure is developed for evaluating the performance of slow-fading WSNs. Three system applications are considered: a distributed detection system, a distributed multiple hypothesis system, and a radar target recognition system.First, we consider sensor power optimization for distributed binary detection systems. The system communicates over slow-fading orthogonal multiple access channels. In earlier work, it was demonstrated that system performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system. We define outage probability for slow-fading system as a long-term performance measure, and analytically develop the detection outage with the given system model.Based on the analytical result of the outage probability, diversity gain is derived and shown to be proportional to the number of the sensor nodes. Then, we extend the optimized power control strategy to a distributed multiple hypothesis system, and enhance the power optimization by exploiting a priori probabilities and local sensor statistics. We also extend outage probability to the distributed multiple-hypotheses problem. The third application is radar waveform design with a new performance measure: Task-Specific Information (TSI). TSI is an information-theoretic measure formulated for one or more specific sensor tasks by encoding the task(s) directly into the signal model via source variables. For example, we consider the problem of correctly classifying a linear system from a set of known alternatives, and the source variable takes the form of an indicator vector that selects the transfer function of the true hypothesis. We then compare the performance of TSI with conventional waveforms and other information-theoretic waveform designs via simulation. We apply radar-specific constraints and signal models to the waveform optimization.
Degree ProgramElectrical & Computer Engineering