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- W2042417105 abstract "Within the Neyman-Pearson framework we investigate the effect of feedback in two-sensor tandem fusion networks with conditionally independent observations. While there is noticeable improvement in performance of the fixed sample size Neyman-Pearson (NP) test, it is shown that feedback has no effect on the asymptotic performance characterized by the Kullback-Leibler (KL) distance. The result can be extended to an interactive fusion system where the fusion center and the sensor may undergo multiple steps of interactions." @default.
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- W2042417105 date "2013-05-01" @default.
- W2042417105 modified "2023-09-23" @default.
- W2042417105 title "Interactive fusion in distributed detection: Architecture and performance analysis" @default.
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- W2042417105 doi "https://doi.org/10.1109/icassp.2013.6638463" @default.
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