We describe an algorithm for class-independent automated target recognition (ATR) and association using range-Doppler images of moving targets and SAR images of stationary targets. This algorithm can be used both for target identification (by comparison against a pre-existing database of measurements of all potential targets) and target association (not requiring a pre-existing database). The algorithm computes a one-dimensional signature for each received range-Doppler image; these signatures are stored in a database for comparison against other detections. The signatures used in our algorithm are range profiles, generated from the clutter-suppressed, filtered image by incoherently integrating the image energy across a number of Doppler bins centered on the target. The result is then normalized, to remove information about the overall cross-section from the profile, and range-aligned with other collected profiles by matching the profile centroids. Statistical models of the profiles are created as the targets are tracked, and newly-created profiles are compared against the existing models by computing the likelihood of the new profile given a particular model.


Statistics and Probability

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