基于稀疏约束最优化的步进频雷达成像技术

Stepped-Frequency Radar Imaging Technology with Sparsity Constrained Optimization Algorithm

步进频雷达发射窄带脉冲,利用多个脉冲合成宽带成像,对发射、接收分系统和信号处理平台的要求相对较低,是获得目标高分辨距离像的一种有效技术手段。针对步进频雷达成像技术在工程应用中存在伪峰、假目标等问题,提出了一种基于稀疏约束最优化的步进频雷达成像技术。将步进频雷达成像处理等效为两个顺序实施的线性反问题,在反问题的最优化求解中引入目标的稀疏性这一先验约束条件,实现了超低副瓣步进频雷达成像,显著缓解了一维距离像的栅瓣问题,且一定程度提高了算法的分辨能力,理论仿真和实际数据测试验证了方法的精度与稳定性。

Stepped-frequency radar transmits narrow band pulsed signals and gets HRRP(High Resolution Range Profile)images through synthesizing multiple pulses.As an effective means to get HRRP of targets,it has relaxed requirements on the performance of transmitters,receivers and signal processing systems.Aiming at the problems of false peaks and loss of signal-to-noise ratio(SNR)which often occur in engineering applications,a new stepped-frequency radar imaging technology based on sparse constraints optimization is proposed.The stepped-frequency radar imaging processing is equivalent to two sequential linear inverse problems.A priori constraint condition of sparsity of the target is introduced into the optimization solution of the inverse problem.The ultra-low sidelobe stepped-frequency radar imaging is realized,which significantly alleviates the false peaks and signal-to-noise ratio loss of one-dimensional range profile.The theoretical simulation and the real data application verify the accuracy and stability of the proposed method.