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Optimal Local Sensor Decision Rule Design for the Channel-Aware System with Novel Simulated Annealing Algorithms

Recently, distributed detection has been intensively studied. The prevailing model for
distributed detection (DD) is a system involving both distributed local sensors and a fusion
center. In a DD system, multiple sensors work collaboratively to distinguish between two or
more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD
problem is reexamined in the context of wireless sensor network applications. For minimize the
error probability at the fusion center, we consider the conventional method that designs the
optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the
transmission channel characteristics for find the optimal binary local sensor decision threshold
to minimize the error probability at the fusion center. And there have different optimal local
sensor decision thresholds for different channel state information. Because of optimal multi-bit
(soft) local sensor decision is more practical than optimal binary local sensor decision.
Allowing for multi-bit local sensor output, we also consider another conventional method that
designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system.
However, to design the optimal local sensor decision rule, both of two conventional methods
are easily trapped into local optimal thresholds, which are depended on the pre-selected
initialization values. To overcome this difficulty, we consider several modified Simulated
Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional
methods, we propose two novel SA algorithms for implementing the optimal local sensor
decision rule. Computer simulation results show that the employments of two novel SA
algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor
decision problem and optimal multi-bit local sensor decision problem. And two novel SA
algorithms offer superior performance with lower search points compared to conventional SA
algorithm.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0818109-171645
Date18 August 2009
CreatorsHsieh, Yi-Ta
ContributorsHSING-HSIUNG YANG, Shiunn-Jang Chern, Jenq-Tay Yuan, Chin-Der Wann, Shyh-Neng Lin
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0818109-171645
Rightswithheld, Copyright information available at source archive

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