基于混合体制雷达网的弹道目标微特征及外形参数提取

针对弹道中段目标微特征难以识别与分辨的问题,提出了一种基于低分辨雷达和高分辨雷达相结合的混合体制雷达网的有翼弹道目标微特征及外形参数提取方法。依据非线性信号参量可分离模型,利用非线性最小二乘估计方法解算出有翼弹道目标群各散射中心的幅相参数,结合不同雷达提取的微特征的关联性,利用散射中心关联处理实现了各类散射中心的分离。在此基础上,利用弹道目标的微特征,结合弹道目标各散射中心的相对位置关系,重构出各目标的三维微特征及各散射中心的三维位置矢量,进而估计出目标的进动特征和结构参数。仿真结果表明:当信噪比(SNR)为5 dB时,该方法的重构精度保持在92%左右。

Micro-motion feature and shape parameters extraction based on hybrid-scheme radar network for ballistic targets

Aiming at the complexity of recognition and resolution on ballistic mid-course target, a method for three-dimensional reconstruction of ballistic target based on hybrid-scheme radar network combined low-resolution radar with high-resolution radar is proposed. Based on the separable model of nonlinear signal parameter, the amplitude-phase parameters of each scattering center on the ballistic target group with empennages are calculated by nonlinear least squares estimation method. Combined with the relationship of micro-motion features between radars, various scattering centers are separated by association processing between scattering centers. Ultimately, the three-Dimensional micro-motion features and the three-Dimensional position vectors are reconstructed by utilizing both the micro-Doppler characteristics and the relative position relation of each scattering center of ballistic target, and then the precession feature and structural parameters are estimated. Simulation results validate that the reconstruction precision of three-dimensional features has been maintained at about 92% when the signal noise ratio (SNR) is 5 dB.