基于宽窄带微多普勒信息的进动目标特征提取

微动特征是弹道目标识别的重要特征之一。针对锥体目标模型，提出了一种基于宽窄带相结合的混合体制雷达网的微动参数提取方法。首先，在详细分析锥体目标等效散射中心微多普勒变化规律的基础上，利用微多普勒和差比实现了不同体制雷达回波中散射中心的匹配关联。其次，构建宽窄带微多普勒信息联合方程组，提取出锥体目标的进动角、底面半径、锥体高度等参数，并进一步对目标参数估计精度随曲线参数提取误差变化的关系做了比较研究。最后，仿真结果表明，在曲线参数提取值存在一定误差时，目标参数估计精度仍然较高。

Feature extraction of precession targets based on wideband and narrowband micro-Doppler information

Micro-motion feature is one of the crucial features used for ballistic target recognition. Aimed at the model of cone-shaped target, a novel algorithm based on hybrid-scheme radar network combining wideband radar with narrowband radar is proposed to extract the micro-motion parameters. First, on the basis of analyzing the micro-Doppler change rule of the equivalent scattering centers on the precession cone-shaped target in detail, each scattering center in different system radar echoes is matched and identified by utilizing the micro-Doppler sum-difference ratio. Second, the associated systems of equations of micro-Doppler information are established, and parameters including the precession angle, radius of undersurface and height of cone-shaped target are extracted jointly. Furthermore, comparative study on the relationship between parameter estimation accuracy and error change of curve parameter extraction is made. Finally, the simulation results show that even though errors occur in curve parameter extraction, the parameter estimation accuracy of target is still enough.