窄带雷达网弹道目标微动特征提取

Micro-motion Feature Extraction of Ballistic Targets Based on Narrowband Radar Network

目标微动特征是弹道中段识别的有效特征之一。针对单部雷达获取目标微动信息的局限性,提出了一种利用窄带雷达网进行弹道目标进动特征提取的方法。首先,建立了锥体进动模型和窄带信号模型,得到了散射点微多普勒表达式。然后,在锥体非理性散射点转化为理想散射点的基础上,通过频谱分析,实现了不同视角下散射点的匹配关联。最后,利用锥顶微多普勒信号对锥底进行补偿,在雷达视角方差最小时求得补偿系数。再联立2部雷达的微多普勒信息即可求出参数。仿真结果表明该方法能够精确提取微动参数和结构参数。

Targets＇ micro-motion feature is one of the effective features used for recognition at the middle section of the ballistic curve. Aimed at the problem that a single radar is rather limited in extracting micro- motion information, this paper proposes a novel algorithm based on the narrowband radar network to ex- tract precession features. First, a cone-shaped target model and a narrowband signal model are estab- lished. Then, each scattering point in different perspective is matched by frequency analysis based on transforming non-ideal scattering point into ideal scattering point. Finally, by using the micro-Doppler of conic node to compensate the bottom micro-Doppler of cone, compensation coefficient is solved when the radar aspect variance is minimal. Furthermore, parameters are obtained by combining the micro-Doppler of two radars. The simulation results show that the algorithm can extract micro-motion parameters and struc- tured parameters accuracy.