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Multispectral (IR and MMW) processing for automatic target detection

Automatic Target Recognition algorithms have been developed with limited success over the last few years. The processing to extract military targets from background clutter has difficulty under noisy, real-world conditions. Fusion of data from different wavelength sensors has been one approach to improve performance. The underlying theory is that signature data from different areas of the electro-magnetic spectrum will be complementary and clutter is frequency dependent. Recent work based on both statistical classification, and feature analysis in the thermal infrared and millimeter wave spectra, has shown interesting trends. We will provide a description of the IR/MMW target classification algorithms, the fusion architecture we employed, and processes used to search for the optimum features. Two distinct search and detect schemes were tested with different results. Data acquisition and reduction issues which affect algorithm experiments will also be discussed. A software based algorithm development test-bed was built at Textron to implement the multispectral targeting experiments. The effect of a modular, programmable test-bed on such experiments is to increase productivity and allow multivariate evaluatio ns.