基于FVE法的FDA-MIMO雷达主瓣密集假目标干扰抑制

FDA-MIMO radar mainlobe dense false target jamming suppression based on feature vector eliminating

针对频率分集多输入多输出(FDA-MIMO)雷达在目标导向矢量失配、训练数据包含期望信号的情况下,主瓣密集假目标干扰抑制性能急剧下降的问题,根据FDA-MIMO信号模型,给出了FDA-MIMO雷达密集假目标干扰抑制机理,提出了一种基于特征向量剔除法(FVE)的干扰抑制方法.仿真结果表明,与特征空间投影(ESB)方法相比,该方法在低信噪比和低快拍的条件下仍能输出较高的信干噪比,提升了主瓣干扰的抑制性能.

In order to solve the problem of a sharp decrease in the performance of suppressing dense false target jamming within mainlobe when the target steering vector is mismatched and the training data contains the desired signal in the radar of multiple-input multiple-output with frequency diverse array(FDA-MIMO),this paper,based on the FDA-MIMO signal model,gives the mechanism of FDA-MIMO radar suppressing dense false target jamming,and proposes an anti-jamming method based on feature vector elimination(FVE).The simulation result shows that compared with eigenspace-based beamformer(ESB)algorithm,this method can still output a higher SINR under the condition of low SNR and few snapshots,and improves the performance of suppressing the mainlobe jamming.