This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and...This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio(SNR)of pulse compression.In this paper,we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal(RAMS)model firstly.Then the corresponding algorithm improved blind source separation(BSS)using the frequency domain of robust principal component analysis(FDRPCA-BSS)is proposed based on the established rotating model.It can eliminate the influence of the rotating parts and address the problem of loss of SNR.Finally,the measured peakto-average power ratio(PAPR)of each separated channel is performed to identify the target echo channel among the separated channels.Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.展开更多
近年来,鲁棒主成分分析法(Robust Principal Component Analysis,RPCA)被广泛应用到运动目标检测中,但该类方法未能有效利用运动目标的时空连续性先验,容易将动态背景误判为运动目标,且背景恢复精度不高.为此提出一种基于全变分-核回归...近年来,鲁棒主成分分析法(Robust Principal Component Analysis,RPCA)被广泛应用到运动目标检测中,但该类方法未能有效利用运动目标的时空连续性先验,容易将动态背景误判为运动目标,且背景恢复精度不高.为此提出一种基于全变分-核回归的RPCA运动目标检测方法.该方法以RPCA为基础,利用3维全变分模型增强前景的时空连续性,去除动态背景干扰,得到清晰完整的前景.同时,利用基于扩散张量的核回归对背景的时空相关性建模,去除噪声干扰,从而精确恢复背景.在多组公开数据集上的实验结果表明,该方法在动态背景、光照变化等复杂场景中能够较为精确地检测出运动目标和恢复背景.展开更多
鲁棒主成分分析(Robust principal component analysis,RPCA)模型中秩函数和L0范数的求解是非确定性多项式(Nondeterministic polynominal,NP)难问题,凸近似模型的求解通常会导致过收缩。本文结合加权方法和Lp范数提出了一种基于双加权L...鲁棒主成分分析(Robust principal component analysis,RPCA)模型中秩函数和L0范数的求解是非确定性多项式(Nondeterministic polynominal,NP)难问题,凸近似模型的求解通常会导致过收缩。本文结合加权方法和Lp范数提出了一种基于双加权Lp范数的RPCA模型,利用加权S p范数低秩项和加权Lp范数稀疏项分别对RPCA框架中的低秩恢复问题和稀疏恢复问题进行建模,使其更接近秩函数和L0范数最小化问题的解,提升了矩阵秩估计和稀疏估计的准确性。为了验证模型性能,本文利用图像的非局部自相似性,结合相似图像块组的低秩性与椒盐噪声的稀疏性,将双加权Lp范数鲁棒主成分分析模型应用于去除椒盐噪声过程中。定量与定性的实验结果表明,本文模型性能优于其他模型,同时奇异值过收缩分析也表明本文模型能够有效抑制秩成分的过度收缩。展开更多
基金supported by the National Natural Science Foundation of China(62271255,61871218,61801211)the Fundamental Research Funds for the Central Universities(3082019NC2019002,NG2020001,NP2014504)+2 种基金the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology(2018_SGIIT_KFJJ_AI_03)the Funding of Postgraduate Research Practice&Innovation Program of Jiangsu Province(KYCX200201)the Open Research Fund of the Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of E ducation(NJ20210001)。
文摘This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio(SNR)of pulse compression.In this paper,we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal(RAMS)model firstly.Then the corresponding algorithm improved blind source separation(BSS)using the frequency domain of robust principal component analysis(FDRPCA-BSS)is proposed based on the established rotating model.It can eliminate the influence of the rotating parts and address the problem of loss of SNR.Finally,the measured peakto-average power ratio(PAPR)of each separated channel is performed to identify the target echo channel among the separated channels.Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.
文摘近年来,鲁棒主成分分析法(Robust Principal Component Analysis,RPCA)被广泛应用到运动目标检测中,但该类方法未能有效利用运动目标的时空连续性先验,容易将动态背景误判为运动目标,且背景恢复精度不高.为此提出一种基于全变分-核回归的RPCA运动目标检测方法.该方法以RPCA为基础,利用3维全变分模型增强前景的时空连续性,去除动态背景干扰,得到清晰完整的前景.同时,利用基于扩散张量的核回归对背景的时空相关性建模,去除噪声干扰,从而精确恢复背景.在多组公开数据集上的实验结果表明,该方法在动态背景、光照变化等复杂场景中能够较为精确地检测出运动目标和恢复背景.
文摘传统主元分析(Principal component analysis,PCA)方法因忽视量纲对系统的影响,从而使选取的主元难以具有代表性;而在进行量纲标准化后,又因得到的特征值常常是近似相等的而无法进行有效的主元提取.针对这一主要问题,本文通过引入相对化变换(Relative transform,RT)、相对主元(Relative principal components,RPCs)和分布"均匀"等概念,建立起一种相对主元分析(Relative principal component analysis,RPCA)的新方法.该方法首先对系统各分量进行量纲标准化;其次再根据系统的先验信息分析和确定各分量的重要程度;然后在系统能量守恒的准则下,赋以系统各分量相应的权值;最后利用已建立起的相对主元模型,对系统实施RPCA.同时运用数值例子,开展了RPCA在数据压缩和系统故障诊断中的应用研究.理论分析和仿真实验均表明,采用RPCA方法选取出的主元更具代表性和显著几何意义,加之选取主元的灵活性,将使新方法具有更广泛的应用前景。
