Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely us...Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.展开更多
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
旋转机械的剩余使用寿命(remaining useful life, RUL)预测对工业设备预测和健康管理的具有重要意义。该文针对多传感器冗余数据导致旋转机械退化信息提取困难、剩余使用寿命预测效果差的问题,提出了一种基于核主成分分析-长短期记忆网...旋转机械的剩余使用寿命(remaining useful life, RUL)预测对工业设备预测和健康管理的具有重要意义。该文针对多传感器冗余数据导致旋转机械退化信息提取困难、剩余使用寿命预测效果差的问题,提出了一种基于核主成分分析-长短期记忆网络(kernel principal component analysis-long short term memory, KPCA-LSTM)的方法对旋转机械剩余使用寿命预测。首先,分析旋转机械的多维退化数据,选择可以表征旋转机械退化的数据;其次,对退化数据进行(kernel principal component analysis, KPCA)融合及特征提取,将降维融合的特征作为预测模型的输入;然后构建旋转机械的健康指标,并通过多阶微分划分旋转机械的不同健康状态,建立KPCA-LSTM模型对旋转机械的剩余使用寿命进行预测;最后,在实验室搭建的矿用减速器平台上进行了试验验证。试验结果表明:该文所提方法与LSTM、粒子群优化LSTM的方法比较,该方法预测效果优于其他两种模型,并降低模型训练的复杂性,减少预测用时。展开更多
基金supported by the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2011AA040202)the National Natural Science Foundation of China(Grant No.40976114)
文摘Discriminating internal layers by radio echo sounding is important in analyzing the thickness and ice deposits in the Antarctic ice sheet.The signal processing method of synthesis aperture radar(SAR)has been widely used for improving the signal to noise ratio(SNR)and discriminating internal layers by radio echo sounding data of ice sheets.This method is not efficient when we use edge detection operators to obtain accurate information of the layers,especially the ice-bed interface.This paper presents a new image processing method via a combined robust principal component analysis-total variation(RPCA-TV)approach for discriminating internal layers of ice sheets by radio echo sounding data.The RPCA-based method is adopted to project the high-dimensional observations to low-dimensional subspace structure to accelerate the operation of the TV-based method,which is used to discriminate the internal layers.The efficiency of the presented method has been tested on simulation data and the dataset of the Institute of Electronics,Chinese Academy of Sciences,collected during CHINARE 28.The results show that the new method is more efficient than the previous method in discriminating internal layers of ice sheets by radio echo sounding data.
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.
文摘旋转机械的剩余使用寿命(remaining useful life, RUL)预测对工业设备预测和健康管理的具有重要意义。该文针对多传感器冗余数据导致旋转机械退化信息提取困难、剩余使用寿命预测效果差的问题,提出了一种基于核主成分分析-长短期记忆网络(kernel principal component analysis-long short term memory, KPCA-LSTM)的方法对旋转机械剩余使用寿命预测。首先,分析旋转机械的多维退化数据,选择可以表征旋转机械退化的数据;其次,对退化数据进行(kernel principal component analysis, KPCA)融合及特征提取,将降维融合的特征作为预测模型的输入;然后构建旋转机械的健康指标,并通过多阶微分划分旋转机械的不同健康状态,建立KPCA-LSTM模型对旋转机械的剩余使用寿命进行预测;最后,在实验室搭建的矿用减速器平台上进行了试验验证。试验结果表明:该文所提方法与LSTM、粒子群优化LSTM的方法比较,该方法预测效果优于其他两种模型,并降低模型训练的复杂性,减少预测用时。
