Local correlations as a translationally invariant feature space for target detection

An approach is presented to the problem of target detection under conditions of low signal-to-noise ratio. A feature space is constructed from local correlation properties of a model one-dimensional image. A multilayer back-propagation network is trained using these features. A target is embedded in noise, either uncorrelated or pairwise correlated. The error rate for target detection is higher for correlated than for uncorrelated noise when using only a pairwise-correlated feature space. Adding triplet correlated features has no effect on the error rate for the case of uncorrelated noise, but for the pairwise correlated noise the additional features reduce the error rate to that of the uncorrelated noise