Distributed Detection in Dependent Gaussian Mixture Noise
نویسنده
چکیده
Finding optimum distributed detection schemes is a diicult mathematical problem. Cases with dependent non-Gaussian impulsive noise are of particular interest and have not yet been studied. Here a two-sensor known-signal detection problem is considered where additive impulsive noise, which is dependent from sensor to sensor, corrupts the observations. The noise is modeled as a mixture of Gaussian distributions , a typical model for impulsive noise. A criterion of Bayes risk is adopted for cases with xed fusion rules. The optimum sensor tests are shown to be diierent from the best isolated sensor tests (likelihood ratio tests) in several cases. Further, a methodology for predicting the form of the optimum sensor tests is presented. This includes predicting when and how the optimum sensor tests diier from the optimum isolated sensor tests.
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