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Distributed detection in dependent gaussian mixture noise.
| Content Provider | CiteSeerX |
|---|---|
| Author | Vikalo, H. Blum, R. S. |
| Abstract | Finding optimum distributed detection schemes is a difficult 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 fixed fusion rules. The optimum sensor tests are shown to be different 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 differ from the optimum isolated sensor tests. 1 INTRODUCTION Signal detection schemes which use distributed processing of observations taken from multiple sensors have important applications i... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Dependent Gaussian Mixture Noise Optimum Sensor Test Particular Interest Multiple Sensor Optimum Distributed Detection Scheme Introduction Signal Detection Scheme Typical Model Impulsive Noise Distributed Processing Isolated Sensor Test Two-sensor Known-signal Detection Problem Optimum Isolated Sensor Test Gaussian Distribution Dependent Non-gaussian Impulsive Noise Likelihood Ratio Test Fixed Fusion Rule Additive Impulsive Noise Bayes Risk Several Case Important Application Difficult Mathematical Problem |
| Content Type | Text |
