基于形态成分分析的随机调制雷达有源假目标抑制方法研究

Based on combination of compressed sensing theory and random modulated signal, the random modulated compressed sensing radar (RMCSR) can improve the performance of target detection and imaging within shorter coherent processing time by exploiting the sparsity of target echoes. However, under electronic countermeasure environments such as the coherent false target jamming, the sparsity of target echoes will be destroyed and the RMCSR’s superiority will be limited. In practice, due to the random modulation, the jammers can hardly update RMCSR’s transmitted signal and parameters in time exactly, which will induce that the echo signals of true and false targets are sparse in different dictionaries. Based on this fact and referring to the idea of morphological component analysis (MCA), this proposal presents a novel strategy for jamming suppression, i.e., by applying sparse representation of RMCSR echo signal in the union of two kinds of dictionaries, the true and false targets can then be separated. Under the framework of compressed sensing, we will mainly focus on two issues here, sparse dictionary design and sparse model solution, aiming at suppressing the false target jamming. Finally, we will try to answer the key question: for a certain kind of false target jamming, which form of RMCSR should be chosen and how dose it work? The results of this investigation will provide important theoretical and technical supports for pushing forward the progress of the utilization of RMCSR.

随机调制压缩感知雷达巧妙结合随机雷达信号和压缩感知理论的优势，充分利用目标回波的稀疏性，在较短的相参处理时间内有效提高了目标检测与成像性能。然而，实际对抗中有源相干假目标等干扰措施将破坏回波稀疏性，使其优越性能难以发挥。由于调制的随机性，现实中干扰机往往无法实时准确地更新所侦察的雷达信号全部参数，这将导致真实目标信号和虚假目标信号在不同的词典上稀疏。本项目利用这一特性，借鉴形态成分分析的思想，提出将雷达回波在这两类词典形成的联合词典上稀疏表示从而实现真/假目标信号分离这一崭新的抗有源假目标干扰思路。拟在压缩感知框架下，深入研究针对假目标识别抑制的稀疏词典设计和稀疏模型求解两大方面的内容，最终回答“何种随机调制雷达能够在多大程度上对抗何种有源假目标”这一核心问题，研究成果将为随机调制压缩感知雷达的实战化应用提供理论和技术支撑。

本项目针对运动有源假目标、高逼真有源假目标和间歇采样转发干扰，按照“干扰信号建模——干扰识别和抑制——抗干扰效果评估”的思路研究了随机调制雷达对这三类典型有源假目标干扰的识别和抑制。首先，针对运动有源假目标，建立随机脉冲重复间隔和随机脉冲初始相位这两种典型的随机调制信号的干扰模型，运用形态成分分析的手段实现了目标信号和干扰信号的识别与分离；其次，分别针对成像有源假目标和微动有源假目标，建立了特定脉间随机调制下的干扰模型，运用形态成分分析的手段实现了目标信号和干扰信号的分离；最后，针对间歇采样转发干扰，建立随机脉内频率编码信号的干扰模型，在认知框架下，运用时频分析和时域滤波等手段实现对干扰参数的估计和对假目标的抑制。以项目研究成果为基础，出版专著1部，发表学术论文13篇，SCI检索6篇，申请发明专利3项。