搜索资源列表
musicF1
- 任意阵列输出数据的协方差进行奇异值分解，求出特征值和特征向量，利用信号子空间和噪声子空间的正交性，构造空间谱函数-Arbitrary array output data covariance singular value decomposition, eigenvalues and eigenvectors obtained using the signal subspace and noise subspace orthogonality spectral function space con
classical_music
- 经典的MUSIC算法，由特征值分解得到噪声子空间，再进行谱峰搜索-Classic MUSIC algorithm, the eigenvalue decomposition noise subspace, then the peak search
MUSIC
- 分别用工具箱里的库函数和自己编写的利用噪声子空间法算法作MUSIC估计-Toolbox respectively in the preparation of library functions and their use of the noise subspace for the MUSIC algorithm estimates
GUI
- Descr iption The MUSIC algorithm, proposed by Schmidt, first estimates a basis for the noise subspace and then determines the peaks the associated angles provide the DOA estimates. The MATLAB code for the MUSIC algorithm is sampled by creating an
MUSIC_algorithm
- 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 su
EVD
- 用子空间分解法求出时延估计，这种方法具有较强的抗噪声性能-Calculated using subspace decomposition delay estimation, this method has strong anti-noise performance
Array-processing
- 阵列处理程序，40阵元，3信源，实现信号子空间拟合，噪声子空间拟合-Array processing, array element 40, 3 sources, to achieve signal subspace fitting, noise subspace fitting
music
- 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
music
- 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 o
fast-subspace-algorithm
- 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M
MIMORadarDOAEstimation
- 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
doa_music
- 阵列协方差矩阵的特征分解DOA估计算法，利用信号子空间与噪声子空间的正交特性-The array covariance matrix of the characteristic decomposition of the DOA estimation algorithm using the orthogonal properties of the signal subspace and noise subspace
noise_subspace_fitting
- 单电磁矢量传感器噪声子空间拟合算法-空间谱图-Single electromagnetic vector sensor noise subspace fitting algorithm- space spectra
NSF_RMSE_SNR
- 单电磁矢量传感器噪声子空间拟合算法-RMSE随着SNR变化曲线-Single electromagnetic vector sensor noise subspace fitting algorithm-RMSE curve with the SNR changes
Subspace-Tracking
- Subspace Tracking in Colored Noise Based on Oblique Projection
yanchixiangjia
- 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
doa_music
- DOA估计的传统经典之作，利用噪声子空间和信号子空间正交性，得到高分辨的拨打估计。-DOA estimation of the traditional classic, using the noise subspace and signal subspace orthogonality, get high resolution to estimate.
BIDIRECTIONAL_SMOOTHNESS_MUSIC
- 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 vie
classical_music_1
- 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 vie
understanding the signal and noise subspace
- This is a very useful paper to understand signal and noise subspace