基于稀疏表示的小目标检测

Small target detection based on sparse representation

现代战场作战环境复杂,智能化、网络化的干扰机是雷达探测的主要威胁。对于从副瓣进来的干扰,利用阵面空域自由度可以较容易地抑制干扰。但对于从主瓣方向进来的干扰,传统的反干扰方法失效,不能有效抑制干扰。干扰环境下,目标信噪比(SNR)较低,如果降低门限检测会增加很多虚警点迹。针对主瓣噪声干扰场景下小目标检测问题,提出了基于稀疏表示的检测技术,利用了目标可以稀疏表示,而噪声不能被稀疏表示的特性,达到了抑制噪声,降低虚警的效果。仿真实验验证了该方法的有效性。

In modern warfare, the battlefield environment is very complicated. Automatic and intelligent jamming is the main threat for the precise detection of radar systems. Methods based on freedom in array spatial domain can suppress the side-lobe jamming effectively. However, for the main-lobe jamming, traditional anti-jamming methods fail to reduce the negative impact. Under the interfering circumstances, the target’s Signal to Noise Ratio(SNR) is low. Lower detection threshold will cause more false alarm points. In this paper, a detection technique based on sparse representation is proposed for small target detection under jamming of main lobe noise. This detection method uses the characteristics that the target can be represented sparsely but the noise cannot, so as to suppress the noise and reduce the false alarm rate. The simulation results show the effectiveness of the proposed method.