共查询到18条相似文献,搜索用时 220 毫秒
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多重信号分类(MUSIC)算法是一种经典的空间谱估计算法。该文以L型阵列为例,针对2D-MUSIC算法在接收信号信噪比较小时对多个目标中方位相近的目标无法进行准确估计的问题,提出一种改进2D-MUSIC算法。该算法对经典2D-MUSIC算法所构成的协方差矩阵进行共轭重组,并将重组后矩阵的平方与原协方差矩阵的平方进行相加求平均,由此获得新的矩阵,再对该矩阵对应的噪声子空间进行加权处理,选取适当的加权系数构造新的噪声子空间,最后通过谱峰搜索识别出目标位置。计算机仿真结果表明,与2D-MUSIC算法相比,改进后的算法在接收信号信噪比较小时对多个目标中方位相近的目标也能够进行信号波达方向(DOA)估计,提高了L型阵列2维DOA估计的分辨率,具有较好的工程应用价值。 相似文献
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本文系统地研究了信号极点提取的最小二乘法及其改善这类方法性能的重要手段-矩阵低秩逼近。同时,文中还提出一种提取信号极点的矩阵预测到最小二乘法。在此基础上,本文进一步给出了这类方法的一系列算法,并对诸算法作了计算机模拟和性能评价。 相似文献
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针对冲击噪声下因接收信号二阶及以上矩不存在而产生性能恶化的问题,提出一种基于QR分解和鲁棒性主成分分析法(QR-RPCA)的双基地多输入多输出(MIMO)雷达参数估计方法。针对RPCA算法适用于实数矩阵处理的情况,先将复数信号转化为实数;然后根据冲击噪声的稀疏特点与目标信号矩阵的低秩特点,利用QR-RPCA算法将低秩信号矩阵从受冲击噪声污染的接收信号中提取出来,并直接得到信号子空间,该算法避免了传统RPCA算法中的大规模奇异值分解,时间复杂度有所降低;最后根据信号子空间并利用旋转不变信号参数估计技术(ESPRIT)对目标方位进行估计。理论与仿真表明,本文算法相较于其他消除冲击噪声的算法,对于低特征指数的冲击噪声具有更好的估计性能。 相似文献
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信噪比(SNR)是现代通信信号处理中一个重要参数,许多算法需要它作为先验信息以获取最佳估计性能。针对单输入多输出(SIMO)系统的信噪比估计问题,本文提出了一种盲信噪比估计算法。该算法利用多路信号协方差矩阵的奇异值分解(SVD),通过计算矩阵的最大特征值实现各路信号信噪比估计。该算法无需知道信号的先验信息,能够对加性高斯白噪声信道(AWGN)和多径信道下常用的数字调制信号进行信噪比估计。仿真结果表明该算法具有良好的估计性能。与单路信号中基于SVD信噪比估计算法相比,该算法无需估计信号空间与噪声空间维数,提高了估计精度,同时大大减小计算复杂度。 相似文献
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MUSIC算法是一种空间谱估计算法,在对宽带信号进行空间谱估计时,该算法需要较长的观测时间来估计协方差矩阵,不利于高速运动目标的定位。提出了基于驾驶协方差矩阵(STCM)的MUSIC算法,该算法首先对每个频带的CSDM进行特征分解,然后利用各频带的噪声子空间求得噪声空间的STCM,进而利用噪声空间的STCM直接得到整个宽带信号的空间谱估计结果。仿真表明该算法在保证高分辨率的同时,需要较短观测时间,适用于较低信噪比、具有较小观测方差。 相似文献
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基于EMEMP的雷达二维信号融合成像新方法 总被引:2,自引:0,他引:2
雷达信号融合成像是一种能显著提高成像分辨率的参数化新方法.基于改进的Root-Music的传统融合方法对噪声敏感, 且存在模型极点失配的问题.本文通过将MEMP(Matrix Enhancement and Matrix Pencil)的二维频率估计方法推广到稀疏数据域,提出了一种基于扩展矩阵增强矩阵束(EMEMP)的融合新方法.此方法首先构造每一维联合增强矩阵,使其满足MEMP算法的配对要求,然后利用MEMP方法估计模型极点,进行极点配对,然后估计模型系数,最后内插频谱以达到融合的目的.实验结果表明相对于传统融合方法,该方法解决了极点失配的问题,提高了模型参数估计的稳健性. 相似文献
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针对存在加性高斯白噪声多参数变量的多谱线自旋回波串(Spin Echo Train,SET)信号参数估计问题,提出基于特征向量的2-D参数估计方法.将SET信号构造成2-D数据矩阵,按照不同的方式构造Hankel块矩阵束,利用子空间转移不变结构解得特征向量,依据特征向量的结构规律获得衰减因子和频率,基于最小二乘方法进一步获得信号幅度估计.该方法具有自动配对的能力,在相对高信噪比以及频率可分辨的情况下能够实现参数的有效估计,且计算复杂度较低.仿真数据结果证明了算法的有效性. 相似文献
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Several algorithms for estimating generalized eigenvalues (GEs) of singular matrix pencils perturbed by noise are reviewed. The singular value decomposition (SVD) is explored as the common structure in the three basic algorithms: direct matrix pencil algorithm, pro-ESPRIT, and TLS-ESPRIT. It is shown that several SVD-based steps inherent in the algorithms are equivalent to the first-order approximation. In particular, the Pro-ESPRIT and its variant TLS-Pro-ESPRIT are shown to be equivalent, and the TLS-ESPRIT and its earlier version LS-ESPRIT are shown to be asymptotically equivalent to the first-order approximation. For the problem of estimating superimposed complex exponential signals, the state-space algorithm is shown to be also equivalent to the previous matrix pencil algorithms to the first-order approximation. The second-order perturbation and the threshold phenomenon are illustrated by simulation results based on a damped sinusoidal signal. An improved state-space algorithm is found to be the most robust to noise 相似文献
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A new approach for active-sonar target detection and bearing estimation from a mobile two-dimensional array of sensors operating in a predominantly noisy environment is presented. Sensor-level adaptive noise cancellation featuring an unconventional method for reference-noise estimation is the key preprocessing step in the proposed approach. A signal-subspace algorithm resulting from two-stage optimisation based on a generalised eigendecomposition of the signal plus (residual) noise covariance matrix is employed to estimate the bearing of the detected target. Simulation results conclusively demonstrate that the proposed scheme is capable of performing target detection and the subsequent two-dimensional bearing estimation with a high degree of reliability at signal-to-noise power ratios as low as -70 and -40-dB, respectively. 相似文献
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Parthasarathy H. Prasad S. Joshi S.D. 《Signal Processing, IEEE Transactions on》1995,43(10):2346-2360
Two algorithms are proposed for estimating the quadratically coupled frequency pairs (QC pairs) in a signal consisting of complex sinusoids in white noise. Three matrices are constructed from the complex third-order cumulants of the noisy signal, the second and third being time shifted versions of the first. The list of coupled frequencies is obtained from the rank reducing numbers of the matrix pencil formed from the first matrix and either of the latter two. The first algorithm then pairs these components by relating quadratic coupling to the intersection of generalized eigenspaces corresponding to two of these frequencies. The coupling strengths are also obtained in terms of generalized eigenvectors in this intersection space. The second algorithm constructs a two-parameter matrix pencil using all the three matrices. The rank reducing pairs of this pencil on the unit circle yield the QC pairs and the associated generalized eigenvectors: the coupling strengths 相似文献
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Most of the existing algorithms for parameter estimation of damped sinusoidal signals are based only on the low-rank approximation of the prediction matrix and ignore the Hankel property of the prediction matrix. We propose a modified Kumaresan-Tufts (MKT) algorithm exploiting both rank-deficient and Hankel properties of the prediction matrix. Computer simulation results demonstrate that compared with the original Kumaresan-Tufts (1982) algorithm and the matrix pencil algorithm, the MKT algorithm has a lower noise threshold and can estimate the parameters of signal with larger damping factors 相似文献