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In this paper, q-chirplet based signal processing is applied to data from a low-resolution ground surveillance pulse doppler RADAR, to classify three classes of targets: personnel, wheeled vehicles and animals. The authors utilize Zernike Moments (ZM) over the chirplet parameters to determine the pertinent features. Their work provides a new approach for multi-resolution analysis and classification of non-stationary signals with the objective of revealing important features in an unknown noise and clutter environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class.
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