Abstract
Effective recognition of micromotion dynamics, e.g. spinning, precession, and nutation, etc., for typical space targets is of great significance in radar imaging, radar target identification and micromotion parameter estimation. Quite different from the conventional recognition system which requires careful designing of a feature extractor, a data-driven recognition system is designed in this paper based on the theory of deep convolutional networks (CNN). With the joint time-frequency distribution as the input, this system can automatically discover the representations needed for micromotion determination and achieves high recognition performance.
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