Quantization and fusion schemes are derived for multisensor correlation in distributed K-sensor systems that are used for the detection of weak signals or general signal discrimination from dependent observations. Asymptotically optimal memoryless quantization and fusion schemes are derived for problems with dependence in the observations across time and sensors. The results obtained are valid for an arbitrary number of sensors and make it possible to compare the performances of multisensor systems as the number of sensors increases and the correlation in the sensor observations across time and sensors varies. Numerical results based on the simulation of the performance of the proposed schemes with different numbers of sensors are presented. The performance of the optimal nonlinearities and quantizers is shown to be better than that of nonlinearities or quantizers obtained by ignoring the dependence in sensor observations and to improve as the number of sensors increases.
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