基于分布式组网雷达的弹道目标三维进动特征提取

弹道目标微动特征提取是当前研究的一个热点,但在单基雷达中,由于视角限制,仅能提取目标在雷达视线方向上的微动分量,难以获得目标的真实三维微动参数.本文基于分布式组网雷达,利用组网雷达的多视角特性,提出了有翼弹道目标三维进动特征提取方法.首先基于目标锥顶散射点的微多普勒特征参数实现了目标空间三维锥旋矢量的重构,在此基础上,通过分析锥底边缘散射点的进动特征与微多普勒曲线的关系,提取了目标的进动周期、自旋周期、进动角、锥底半径、自旋轴与锥旋轴的交点位置等特征,并实现了目标长度的估计.仿真实验验证了算法的有效性,并进一步利用仿真实验分析了算法的鲁棒性.

Three-Dimensional Precession Feature Extraction of Ballistic Targets in Distributed Radar Networks

The micro-motion feature extraction for ballistic target has attracted great attention in recent years.Because of the limitation of the angle of view,only the characteristics of micro-motion projected in the line-of-sight(LOS) could be extracted in monostatic radars.In the paper,the distributed radar networks are utilized to obtain the three-dimensional micro-motion features of ballistic targets with empennages.Profited from the multi-view of distributed radar networks,the 3-D coning vector of target is reconstructed from the micro-Doppler parameters of the scatterer on the cone apex of ballistic target,and then the precession period,spinning period,precession angle,the radius of cone bottom,and the intersection location of the spinning axis and the conning axis are solved by analyzing the relationship between the micro-Doppler curves and the precession motion of the scatterers on edge of the cone bottom.The length of target is also estimated.Simulations validate the effectiveness of the algorithm,and the robustness of the algorithm is also analyzed.