基于时频分布的空间锥体目标微动形式分类

空间锥体目标的微动形式分类对空间目标识别、参数估计等有着重要意义。针对这一问题，提出了一种从雷达回波时频分布中提取特征对微动形式进行分类的方法。首先分析了空间锥体目标的散射特性，在此基础上建立了等效散射点模型，并与传统的一般散射点模型比较，电磁计算结果进一步证明了提出模型的正确性；在特征提取阶段，基于能量强弱提取了回波时频分布中包含微动信息的区域，并针对自旋、进动、章动3种微动形式下瞬时频率变化的差别提取了4种特征；最后基于等效散射点模型仿真产生训练数据集、电磁计算产生测试数据集的模式，使用支持矢量机（support vector machine, SVM）分类器的分类实验结果表明新方法在一定信噪比条件下可有效实现对微动形式的分类。

Classification of micro-motion form of space cone-shaped objects based on time-frequency distribution

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).