Sensor networks provide virtual snapshots of the physical world via distributed wireless nodes that can sense in different modalities, such as acoustic and seismic. Classification of objects moving through the sensor field is an important application that requires collaborative signal processing (CSP) between nodes. Given the limited resources of nodes, a key constraint is to exchange the least amount of information between them to achieve desired performance. Two main forms of CSP are possible. Data fusion - exchange of low dimensional feature vectors - is needed between correlated nodes, in general, for optimal performance. Decision fusion - exchange of likelihood values - is sufficient between independent nodes. Decision fusion is generally preferable due to its lower communication and computational burden. We study CSP of multiple node measurements for classification, each measurement modeled as a Gaussian (target) signal vector corrupted by additive white Gaussian noise. The measurements are partitioned into groups. The signal components within each group are perfectly correlated whereas they vary independently between groups. Three classifiers are compared: the optimal maximum-likelihood classifier, a data-averaging classifier that treats all measurements as correlated, and a decision-fusion classifier that treats them all as independent. Analytical and numerical results based on real data are provided to compare the performance of the three CSP classifiers. Our results indicate that the sub-optimal decision fusion classifier, that is most attractive in the context of sensor networks, is also a robust choice from a decision-theoretic viewpoint.
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