Description: MUSIC 算法是利用接收数据的协方差矩阵(Rx)分离出信号子空间和噪声子空间，利用信号方向向量与噪声子空间的正交性来构成空间扫描谱，进行全域搜索谱峰，从而实现信号的参数估计。-MUSIC algorithm is used to receive data covariance matrix (Rx) to isolate the signal subspace and noise subspace, using the signal direction vector and noise subspace orthogonality to form the spatial scan spectrum, full-domain search peaks in order to achieve signal The parameter estimation. Platform: |
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Author:超云 |
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Description: 在matlab中实现用music算法求解信号子空间的问题-Implemented in matlab with the music signal subspace algorithm for solving the problem Platform: |
Size: 1024 |
Author:分非得分 |
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Description: 本程序运用bing yang的PAST算法和PASTD算法跟踪信号子空间，并利用MUSIC算法估计信号的波达方向(DOA)-This procedure utilizes the PAST algorithm and the PASTD algorithm (put forward by Bing Yang)track signal subspace, and uses MUSIC algorithm to estimate signal prameter Direction Of Arrival(DOA)
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Size: 2048 |
Author:hehe |
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Description: 雷达或移动通信中的阵列信号处理程序，子空间MUSIC算法，MVDR算法，强相关信号的分离演示等等。-Radar or mobile communication array signal processing, subspace MUSIC algorithm, MVDR algorithm, strong correlation signals seperation ,and so on. Platform: |
Size: 7168 |
Author:王跃明 |
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Description: MUSIC，多重信号分类法，基于子空间分解，是DOA估计中的经典高分辨方法，初学者必须懂的。-MUSIC, multiple signal classification method based on subspace decomposition, is the classic high-resolution DOA estimation method for beginners to understand. Platform: |
Size: 1024 |
Author:三秦游子 |
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Description: MUSIC方法，对相关矩阵分解为噪声子空间和信号子空间，然后搜索得到需要估计的频谱-MUSIC method, the decomposition of the correlation matrix for the noise subspace and signal subspace, then search to get the spectrum to be estimated Platform: |
Size: 1024 |
Author:ly1 |
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Description: MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成-MUSIC algorithm is a feature space based on matrix decomposition method. From the geometric point of view, the observed signal space can be decomposed into signal subspace and noise subspace, it is clear that two spaces are orthogonal. Signal subspace to the data received by the array covariance matrix and the eigenvector corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector corresponding to the composition
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Author:zjc |
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Description: 为了对空间辐射源进行精确定位" 建立了基于任意阵列对多目标源进行二维DOA估计的数学模型。将 MUSIC算法推广到三维空间阵列可以对辐射源进行二维高精度测向,但由于其需要估计接收数据的协方差矩阵和进行特征分解, 因而其计算量较大。利用多级维纳滤波器的前向递推获得信号子空间和噪声子空间，不需要估计协方差矩阵和对其进行特征分解，从而降低了MUSIC算法的计算量。将文中的方法应用于任意阵列的二维DOA估计中进行计算机仿真和实际侧向系统性能验证,实验结果均表明该方法达到了MUSIC算法的性能,但与常规MUSIC算法相比降低了计算量.- In order to space radiation source for precise positioning" based on arbitrary array to multiple target source 2D DOA estimation model. The MUSIC algorithm is extended to three-dimensional space to two-dimensional array of radiation source for high precision direction finding, but due to the need to estimate the covariance matrix and eigenvalue decomposition, and the large amount of calculation. Use of multistage Wiener filter forward recursion received signal and noise subspace, does not need to estimate the covariance matrix and the eigenvalue decomposition, which reduces the computation of MUSIC algorithm. This method is applied to an arbitrary array DOA estimation for computer simulation and actual lateral system performance verification, the experimental results show that this method achieves the performance of MUSIC algorithm, but with the conventional MUSIC algorithm reduces the calculation amount. Platform: |
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Author:于文娟 |
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Description: MIMO雷达模型下一种子空间谱估计方法，采用过估计的方法，以避免信源数估计的问题，直接对数据协方差矩阵进行变换，从而构造了信号子空间投影矩阵和噪声子空间投影矩阵，不需要像经典的MUSIC一样对其进行特征分解，完全避开了在一般非理想情况下MUSIC算法必须面对的识别小特征值与大特征值的麻烦，降低了复杂度，而且该方法不受快拍数的影响，在相干源情况下也能准确的估计目标的入射角，不会出现伪峰。-A subspace based DOA (Direction-Of-Arrival) estimation method for MIMO (Multiple-Input Multiple-Output) radar is studied in this paper. By transforming the data covariance matrix, we can construct the projection matrix of signal subspace and noise subspace. The computation complexity of the algorithm is reduced as it does not need to perform eigendecomposition. By using the over estimated theory, this algorithm avoid estimating the number of signal sources and can keep a good estimate performance in the case of lower SNR (Signal-to-Noise Ratios) and multi-target. Platform: |
Size: 66560 |
Author:Peng Zhe |
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Description: 本程序运用PAST算法估计信号的子空间，MUSIC算法估计波达方向，实现了对信号的入射角度的实时跟踪。-The procedures used PAST algorithm estimates the signal subspace, the MUSIC algorithm to estimate DOA, real-time tracking of the incident angle of the signal. Platform: |
Size: 2048 |
Author:云传月 |
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Description: music算法,首先,根据获得的数据来寻找协方差,确定噪声子空间和信号子空间,谱峰搜索-Music algorithm,first of all,according to receive data to find the covariance, determine the noise subspace and the signal subspace, in searching spectral peak Platform: |
Size: 1024 |
Author:庞敏 |
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Description: MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。-MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components. Platform: |
Size: 1024 |
Author:许新亚 |
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Description: MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。-MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components. Platform: |
Size: 1024 |
Author:许新亚 |
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Description: MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。-MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign Platform: |
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Author:宇哥 |
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Description: MUSIC estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method. This method assumes that a signal, x(n), consists of p complex exponentials in the presence of Gaussian white noise. Given an M \times M autocorrelation matrix, \mathbf{R}_x, if the eigenvalues are sorted in decreasing order, the eigenvectors corresponding to the p largest eigenvalues (i.e. directions of largest variability) span the signal subspace. The remaining M-p eigenvectors span the orthogonal space, where there is only noise. Note that for M = p + 1, MUSIC is identical to Pisarenko harmonic decomposition. The general idea is to use averaging to improve the performance of the Pisarenko estimator. Platform: |
Size: 1024 |
Author:Said |
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Description: 基于子空间方法的联合稀疏恢复，通过MUSIC算法进行结合测试，给出了测试结果-We propose subspace-augmented
MUSIC (SA-MUSIC), which improves on MUSIC such that the
support is reliably recovered under such unfavorable conditions.
Combined with a subspace-based greedy algorithm, known as Orthogonal
Subspace Matching Pursuit, which is also proposed and
analyzed in this paper, SA-MUSIC provides a computationally
efficient algorithm with a performance guarantee. The performance
guarantees are given in terms of a version of the restricted
isometry property. In particular, we also present a non-asymptotic
perturbation analysis of the signal subspace estimation step, which
has been missing in the previous studies of MUSIC. Platform: |
Size: 1049600 |
Author:bigbigtom |
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Description: 多重信号分类（MUSIC）算法，将信号分为信号子空间和噪声子空间，利用特征值估计频率-Multiple signal classification (MUSIC) algorithm, the signal is divided into signal subspace and noise subspace, the use of eigenvalues to estimate the frequency Platform: |
Size: 1024 |
Author:周星月 |
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Description: MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm is a kind of feature space based on the matrix decomposition method.From geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, obviously the two space are orthogonal.Signal subspace by array covariance matrix of the received data and signal of the corresponding eigenvectors, the noise subspace by the covariance matrix of all minimum eigenvalue (noise variance) of the corresponding eigenvectors.) Platform: |
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Author:JOE-y
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Description: 波达方向（DOA）估计的基本问题就是确定同时处在空间某一区域内多个感兴趣的信号的空间位置（即多个信号到达阵列参考阵元的方向角）。最早的也是最经典的超分辨DOA估计方法是著名的MUSIC方法，MUSIC是多重信号分类（Multiple Signal Classification）的英文缩写。它是由R．O． Schmidt于1979年提出来的，由1986年重新发表的。MUSIC算法利用了信号子空间和噪声子空间的正交性，构造空间谱函数，通过谱峰搜索，检测信号的DOA．(The basic problem of DOA estimation is to determine the location of multiple interested signals in a certain area at the same time, that is, the direction angle of multiple signals arriving at the array reference element. The earliest and the most classical super-resolution DOA estimation method is the famous MUSIC method, and MUSIC is the abbreviation of Multiple Signal Classification. It was proposed by R.O.Schmidt in 1979 and was republished in 1986. The MUSIC algorithm takes advantage of the orthogonality between the signal subspace and the noise subspace, and constructs the spatial spectral function. It detects the DOA. of the signal by spectral peak search.) Platform: |
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Author:斗斗 |
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Description: 一种基于信号子空间和噪声子空间的music改进算法(An improved music algorithm based on signal subspace and noise subspace) Platform: |
Size: 1024 |
Author:zd837202512 |
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Search - music signal subspace