摘要
空时自适应处理(STAP)相较于传统的脉冲多普勒雷达信号处理,扩展了信号的处理维度,使得杂波和目标在空时联合域得以区分。基于稀疏表示理论和杂波谱的稀疏性,稀疏恢复STAP(SR-STAP)实现了小样本条件下的杂波抑制。针对SR-STAP方法存在未知偏航角时性能下降的问题,提出了一种基于非线性回归的杂波重构STAP方法。首先,基于SR杂波谱,以离群度为收敛目标迭代地剔除脊外散点,并进行坐标加权的非线性回归,实现杂波脊模型参数的精确估计;然后,基于一次筛选的结果,再次通过非线性回归的方法精确估计杂波谱;最后,基于以上的估计结果完成杂波的重构和抑制。仿真结果验证了该杂波重构STAP方法的有效性,且相较于现有STAP方法,取得了更优的空时频率响应和SINR损失,有效提高了杂波抑制和动目标检测的性能。
Compared with the conventional pulse Doppler radar signal processing,Space-Time Adaptive Processing(STAP)expands the signal processing dimension,so that the clutter and the target can be distinguished in the joint space-time domain.Based on sparse representation theory and the sparsity of clutter spectrum,Sparse Recovery(SR)-based STAP realizes clutter suppression under the condition of a small number of training range cells.Aiming at the performance degradation of SR-STAP methods with unknown yaw angle,a nonlinear regression-based clutter reconstruction STAP method is proposed.Firstly,based on SR clutter spectrum,outlier degree is used as the convergence objective to iteratively eliminate the scatter points deviating from the ridge,and the coordinate weighted nonlinear regression is performed to realize accurate estimation of the parameters of the clutter ridge model.Then,based on the results of the first screening,the clutter spectrum is estimated accurately by the nonlinear regression method again.Finally,the clutter reconstruction and suppression are completed based on the above estimation results.The simulation verifies the effectiveness of the proposed method,and compared with the existing STAP methods,it achieves better space-time frequency response and SINR loss,effectively improving the performance of clutter suppression and moving target detection.
作者
邹帛
王欣
冯为可
朱晗归
李瑶
ZOU Bo;WANG Xin;FENG Weike;ZHU Hangui;LI Yao(Air and Missile Defense College Air Force Engineering University,Xi'an 710000 China)
出处
《电光与控制》
CSCD
北大核心
2022年第9期32-37,共6页
Electronics Optics & Control
基金
国家自然科学基金(62001507)
陕西省高校科协青年人才托举计划(20210106)。
