In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new co...In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new covariance matrix by projecting the covariance matrix of the array data to the signal subspace,leading to the elimination of the noise subspace.After-wards,based on the new covariance matrix after the orthogonal projection,a new sparse representa-tion model is established and employed for DOA estimation.Simulation results demonstrate that compared to other methods,the OPSR method has higher angle resolution and better DOA estima-tion performance in the cases of few snapshots and low SNRs.展开更多
In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eli...In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.展开更多
A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to el...A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.展开更多
This paper proposes the Rice condition numbers for invariant subspace, singular subspaces of a matrix and deflating subspaces of a regular matrix pair. The first-order perturbation estimations for these subspaces are ...This paper proposes the Rice condition numbers for invariant subspace, singular subspaces of a matrix and deflating subspaces of a regular matrix pair. The first-order perturbation estimations for these subspaces are derived by applying perturbation expansions of orthogonal projection operators.展开更多
随着外辐射源雷达技术的快速发展和逐步应用,干扰问题日渐受到关注。本文以对OFDM外辐射源雷达工作性能带来极大影响的线性调频(Linear Frequency Modulation,LFM)干扰为研究对象,根据线性调频干扰信号模型,从理论上分析了多周期线性调...随着外辐射源雷达技术的快速发展和逐步应用,干扰问题日渐受到关注。本文以对OFDM外辐射源雷达工作性能带来极大影响的线性调频(Linear Frequency Modulation,LFM)干扰为研究对象,根据线性调频干扰信号模型,从理论上分析了多周期线性调频干扰对OFDM外辐射源雷达的干扰形态和特性,并推导了干扰带宽与相关时延宽度的关系,进而提出了一种自适应多周期线性调频干扰抑制方法。所提干扰抑制方法首先在频域进行干扰检测并估计线性调频干扰带宽,然后根据干扰带宽在距离多普勒谱上选取慢时间维序列构造干扰子空间,最后通过干扰子空间正交投影实现对多周期线性调频干扰的抑制。在不同干扰强度、干扰带宽以及线性调频周期条件下,以基底改善因子作为评估指标,仿真验证了所提方法的有效性和鲁棒性。展开更多
提出了一种新的宽带DOA估计方法:频域子空间正交性测试方法(TOFS:Test of orthogonality of frequency sub- space)。该方法通过同时测试频域信号共同带宽内各频段噪声子空间与阵列流形之间的正交性来进行DOA估计。与宽带相干信号子空...提出了一种新的宽带DOA估计方法:频域子空间正交性测试方法(TOFS:Test of orthogonality of frequency sub- space)。该方法通过同时测试频域信号共同带宽内各频段噪声子空间与阵列流形之间的正交性来进行DOA估计。与宽带相干信号子空间方法不同,TOFS方法不需要任何初始值的预估及聚焦操作。与宽带非相干信号子空间方法也不同,TOFS方法同时测试各频段噪声子空间与阵列流形之间的正交性。本文仿真了TOFS与IMUSIC、CSSM、TOPS的性能比较。仿真结果表明TOFS方法在中等信噪比以上时有较好的性能,且避免了TOPS方法中常出现的伪峰。展开更多
端元光谱提取是高光谱影像混合像元分解的关键。现有的端元提取方法多是仅利用了影像的光谱信息,忽略了像元间的空间相关性。现有研究基础上,提出了一种结合影像空间和光谱信息的高光谱影像端元光谱自动提取方法(integration of spatial...端元光谱提取是高光谱影像混合像元分解的关键。现有的端元提取方法多是仅利用了影像的光谱信息,忽略了像元间的空间相关性。现有研究基础上,提出了一种结合影像空间和光谱信息的高光谱影像端元光谱自动提取方法(integration of spatial-spectral information based endmember extraction,ISEE)。该方法首先进行影像子空间划分以增强影像局部的光谱信息特征,然后通过特征空间投影分析获得影像候选端元,最后依次在影像空间信息约束下和端元光谱信息约束下进行优化,得到最终的影像端元光谱集。仿真高光谱影像和真实高光谱影像的实验结果表明,结合影像空间和光谱信息的ISEE方法是有效的,且比一些常用方法提取的端元光谱更为准确。展开更多
基金the National Natural Science Foundation of China(No.61701133)the Fundamental Research Funds for the Central Universities(No.D5000210641).
文摘In order to reduce the effect of noises on DOA estimation,this paper proposes a direc-tion-of-arrival(DOA)estimation method using sparse representation with orthogonal projection(OPSR).The OPSR method obtains a new covariance matrix by projecting the covariance matrix of the array data to the signal subspace,leading to the elimination of the noise subspace.After-wards,based on the new covariance matrix after the orthogonal projection,a new sparse representa-tion model is established and employed for DOA estimation.Simulation results demonstrate that compared to other methods,the OPSR method has higher angle resolution and better DOA estima-tion performance in the cases of few snapshots and low SNRs.
基金This work was supported by the National Thousand Talents Program of China, the National Natural Science Foundation of China （Nos. 61473054, 61633006）, and the Fundamental Research Funds for the Central Universities of China （No. DUT15ZD108）.
文摘In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.
基金Supported in part by Chinese Recruitment Program of Global Young Expert,Alexander von Humboldt Research Fellowship of Germany,the Foundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (61074020)
文摘A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
文摘This paper proposes the Rice condition numbers for invariant subspace, singular subspaces of a matrix and deflating subspaces of a regular matrix pair. The first-order perturbation estimations for these subspaces are derived by applying perturbation expansions of orthogonal projection operators.
文摘随着外辐射源雷达技术的快速发展和逐步应用,干扰问题日渐受到关注。本文以对OFDM外辐射源雷达工作性能带来极大影响的线性调频(Linear Frequency Modulation,LFM)干扰为研究对象,根据线性调频干扰信号模型,从理论上分析了多周期线性调频干扰对OFDM外辐射源雷达的干扰形态和特性,并推导了干扰带宽与相关时延宽度的关系,进而提出了一种自适应多周期线性调频干扰抑制方法。所提干扰抑制方法首先在频域进行干扰检测并估计线性调频干扰带宽,然后根据干扰带宽在距离多普勒谱上选取慢时间维序列构造干扰子空间,最后通过干扰子空间正交投影实现对多周期线性调频干扰的抑制。在不同干扰强度、干扰带宽以及线性调频周期条件下,以基底改善因子作为评估指标,仿真验证了所提方法的有效性和鲁棒性。
文摘提出了一种新的宽带DOA估计方法:频域子空间正交性测试方法(TOFS:Test of orthogonality of frequency sub- space)。该方法通过同时测试频域信号共同带宽内各频段噪声子空间与阵列流形之间的正交性来进行DOA估计。与宽带相干信号子空间方法不同,TOFS方法不需要任何初始值的预估及聚焦操作。与宽带非相干信号子空间方法也不同,TOFS方法同时测试各频段噪声子空间与阵列流形之间的正交性。本文仿真了TOFS与IMUSIC、CSSM、TOPS的性能比较。仿真结果表明TOFS方法在中等信噪比以上时有较好的性能,且避免了TOPS方法中常出现的伪峰。
文摘端元光谱提取是高光谱影像混合像元分解的关键。现有的端元提取方法多是仅利用了影像的光谱信息,忽略了像元间的空间相关性。现有研究基础上,提出了一种结合影像空间和光谱信息的高光谱影像端元光谱自动提取方法(integration of spatial-spectral information based endmember extraction,ISEE)。该方法首先进行影像子空间划分以增强影像局部的光谱信息特征,然后通过特征空间投影分析获得影像候选端元,最后依次在影像空间信息约束下和端元光谱信息约束下进行优化,得到最终的影像端元光谱集。仿真高光谱影像和真实高光谱影像的实验结果表明,结合影像空间和光谱信息的ISEE方法是有效的,且比一些常用方法提取的端元光谱更为准确。
