Abstract:

The classification of micro-motion form of space cone-shaped objects is significant to space target discrimination, parameter estimation and so on. Aiming at this problem, an automatic classification method is proposed based on features extracted from the time-frequency distribution of radar echoes from space cone-shaped objects. The scattering properties of space cone-shaped objects are firstly analyzed, based on which the equivalent point-scattering model is established, which is compared with the traditional point-scattering model, and the electromagnetic simulation results show the proposed model is correct. In the feature extraction step the region corresponding to micro-motion in the time-frequency distribution is acquired by an energy threshold, and four features are extracted based on the differences between the instantaneous frequency variations of the three kinds of micro-motions: spin, coning and nutation. Finally, the classification experimental results with simulated training data, electromagnetic computation simulated test data and the support vector machine (SVM) show the proposed method can achieve good classification performance under the test condition of certain signal-to-noise ratio (SNR).