The detection of signals in noise is a critical task for a number of practical signal processing and communication systems. The performance gains that can be achieved by using observations taken at multiple sensors are well known. However, generally is too costly to transmit all the data generated by each sensor to some central location to use in making a signal detection decision. It is therefore of great interest to study distributed detection schemes, which provide acceptable performance while requiring only limited transmissions. Research on distributed signal detection schemes for cases with dependent observations has been lacking. The goal of this project is to develop the foundation of a theory for understanding distributed detection schemes for cases with dependent observations from sensor to sensor. The theory will predict when the optimum distributed processing schemes will be different from the optimum isolated sensor processing schemes. The theory will also explain why this occurs. The approach draws heavily on weak-signal results we have already developed and on recent preliminary results. Specifically, we are attempting to represent the optimum sensor processing in terms of known functions and unknown constants, as we have done in the weak signal cases. Using such a representation should allow one to determine when the optimum isolated sensor processings will be optimum and to explain why the optimum isolated sensor processings are not optimum in some cases. While the focus is on developing much needed theory, the results will be immediately useful in many applications. Furthermore, some emphasis is being devoted to developing design algorithms and to the study of nonparametric distributed detection schemes for cases with uncertain statistical models. Such studies will produce tools and techniques for pressing practical problems.
