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The performance of the mGLRT compared to the optimal independent test should vary depending on the degree of correlation between the sensor observations.
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Their findings show that as the correlation between the sensors' observations increases, the optimal detection scheme cannot use single-interval decision regions at both sensors.
Depending on the degree of correlation between the sensors' observations and the variance of the noise affecting the sensors, the performance of the mGLRT varies when compared to the optimal independent test.
The available data are the sensor observations.
The joint probability density function of the sensor observations is.
When the variances are equal and the sensor observations are highly correlated, either both sensors get the same reading, which is equivalent to a one-sensor observation, or both sensors do not get any.
The sensors' observations are independent.
(i) The sensors' observations are independent.
These include the accuracy of sensors' observations (which is usually modeled as observation noise), the available bandwidth and power resources, the fading characteristics of the wireless channels between sensors and the FC, the fusion rule used by the FC, and the type of multiple access channel (MAC 1 used to communicate the sensors' observations to the FC.
(iii) The noise accompanied with the sensors' observations is Gaussian.
The covariance matrix of the sensors' observations is positive definite.
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