Composite filters for inverse synthetic aperture radar classification of small ships

Pattern recognition of small ship inverse synthetic aperture radar (ISAR) images is considered. This represents a formidable new distortion-invariant pattern recognition problem. New weighted correlation range alignment and weighted multiple-scatterer motion compensation variations to standard image formation steps were developed and used. A new algorithm to determine if an input image is useful was developed and found to be necessary for small ship ISAR data. Initial recognition results using distortion-invariant (composite) filters are presented. These provide new concepts including: use of a validation set and a goodness measure to select filter parameters, use of new output criteria for which a filter in a bank of filters is best for class determination, rejection of decisions on some poor test input images, and use of voting over a time sequence of test inputs.