Multisensor integration and fusion for intelligent machines and systems
Multisensor integration and fusion for intelligent machines and systems
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Decision Fusion
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
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In this paper, we propose a two-layer sensor fusion scheme for multiple hypotheses multisensor systems. To reflect reality in decision making, uncertain decision regions are introduced in the hypotheses testing process. The entire decision space is partitioned into distinct regions of “correct”, “uncertain” and “incorrect” regions. The first layer of decision is made by each sensor indepedently based on a set of optimal decision rules. The fusion process is performed by treating the fusion center as an additional “virtual” sensor to the system. This “virtual” sensor makes decision based on the decisions reached by the set of sensors in the system. The optimal decision rules are derived by minimizing the Bayes risk function. As a consequence, the performance of the system as well as individual sensors can be quantified by the probabilities of correct, incorrect and uncertain decisions. Numerical examples of three hypotheses, two and four sensor systems are presented to illustrate the proposed scheme.