Detection of multiple correlated time series and its application in synthetic aperture sonar imagery
Detecting the presence of a common but unknown signal among two or more data channels is a problem that finds its uses in many applications, including collaborative sensor networks, geological monitoring of seismic activity, radar, and sonar. Some detection systems in such situations use decision fusion to combine individual detection decisions into one global decision. However, this detection paradigm can be sub-optimal as local decisions are based on the perspective of a single sensory system. Thus, methods that capture the coherent or mutual information among multiple data sets are needed. ...
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