An adaptive fusion model for distributed detection systems with unequiprobable sources
Department of Electrical and Computer Engineering
Master of Science
Multisensor data fusion.
Distributed detection system.
Adaptive fusion model.
In a traditional communication system, a single sensor such as a radar or a sonar is used to detect targets. Since the reliability of a single sensor is limited, distributed detection systems in which several sensors are employed simultaneously have received increasing attention in recent years. We consider a distributed detection system which consists of a number of independent local detectors and a fusion center. Chair and Varshney have derived an optimal decision rule for fusing decisions based on. the Baysian criterion. To implement such a rule, the probability of detection PD and the probability of false alarm PF for each local detector must be known. This thesis introduces an adaptive fusion model using the fusion result as a supervisor to estimate the PD and PF The fusion results are classified as "reliable" and "unreliable". Reliable results will be used as a reference to update the weights in the fusion center. Unreliable results will be discarded. The thesis concludes with simulation results which conform to the analysis.
njit-etd1994-029 (45 pages ~ 1,806 KB pdf)
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Created July 21, 2008