基于杂波谱稀疏恢复的空时自适应处理

摘要

在机载雷达体制中,空时自适应处理(STAP)可有效抑制杂波并完成动目标检测.但在实际杂波环境中,由于缺乏独立同分布的训练样本,传统STAP算法性能下降严重.针对这一问题,我们利用STAP体制下杂波在角度-多普勒域上的稀疏性,提出基于稀疏恢复的SR-STAP方法,可在少量训练样本下实现高分辨空时杂波谱及相应杂波协方差矩阵的估计.Mountaintop实际数据和仿真实验均表明,SR-STAP收敛速度更快,从而在实际杂波环境中获得更高的信杂噪比改善.%Space-time adaptive processing (STAP) is an effective tool for detecting moving target in airborne radar system.However,in actual clutter environment, the performance of conventional STAP algorithms will degrade a lot for lacking sufficient independent identically distributed training samples. By exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain, an algorithm called SR-STAP is proposed to obtain super-resolution space-time spectrum as well as the clutter covariance matrix with much less training samples. The results of both Mountaintop real data and simulations have proved that SR-STAP can obtain fast convergence rate and achieve better clutter suppression performance than conventional method in actual clutter scenario.