On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC alg...On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.展开更多
针对相干信号波达方向(Direction of Arrival,DOA)估计,提出了一种改进的多重信号分类(Multiple Signal Classification,MUSIC)算法。首先,利用信号协方差矩阵的两个最大特征值所对应的特征向量,构造出两个Toeplitz矩阵;然后,利用前后...针对相干信号波达方向(Direction of Arrival,DOA)估计,提出了一种改进的多重信号分类(Multiple Signal Classification,MUSIC)算法。首先,利用信号协方差矩阵的两个最大特征值所对应的特征向量,构造出两个Toeplitz矩阵;然后,利用前后向空间平滑思想得到这两个矩阵的无偏估计并求和;最后,利用MUSIC算法从中估计出相干信号DOA。和已有方法相比,该方法无需损失阵列孔径且具有更优的DOA估计性能。展开更多
由于噪声的存在,现有的相干信号波达方向估计算法在低信噪比、小快拍数和小信号间隔条件下,性能下降严重。针对这一问题,本文提出一种基于总体最小二乘法——旋转不变子空间(Total Least Squares-Estimating Signal Parameter via Rotat...由于噪声的存在,现有的相干信号波达方向估计算法在低信噪比、小快拍数和小信号间隔条件下,性能下降严重。针对这一问题,本文提出一种基于总体最小二乘法——旋转不变子空间(Total Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques,TLS-ESPRIT)算法的改进前后向空间平滑方法,对相干信源波达方向(Direction of Arrival,DOA)进行估计。该方法利用了信号的强相关性和噪声的弱相关性,通过时空相关协方差矩阵重构平滑后的阵列协方差矩阵,并将得到的新协方差矩阵应用于TLS-ESPRIT算法进行DOA估计。通过与其他几种传统的解相干算法建模仿真对比,该算法在相干源之间的DOA距离较近、信噪比(Signal Noise Ratio,SNR)较低和快拍数较小的情况下可以更好地估计波达方向,且具备更高的分辨率和精度。展开更多
文摘On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.
文摘针对相干信号波达方向(Direction of Arrival,DOA)估计,提出了一种改进的多重信号分类(Multiple Signal Classification,MUSIC)算法。首先,利用信号协方差矩阵的两个最大特征值所对应的特征向量,构造出两个Toeplitz矩阵;然后,利用前后向空间平滑思想得到这两个矩阵的无偏估计并求和;最后,利用MUSIC算法从中估计出相干信号DOA。和已有方法相比,该方法无需损失阵列孔径且具有更优的DOA估计性能。
文摘由于噪声的存在,现有的相干信号波达方向估计算法在低信噪比、小快拍数和小信号间隔条件下,性能下降严重。针对这一问题,本文提出一种基于总体最小二乘法——旋转不变子空间(Total Least Squares-Estimating Signal Parameter via Rotational Invariance Techniques,TLS-ESPRIT)算法的改进前后向空间平滑方法,对相干信源波达方向(Direction of Arrival,DOA)进行估计。该方法利用了信号的强相关性和噪声的弱相关性,通过时空相关协方差矩阵重构平滑后的阵列协方差矩阵,并将得到的新协方差矩阵应用于TLS-ESPRIT算法进行DOA估计。通过与其他几种传统的解相干算法建模仿真对比,该算法在相干源之间的DOA距离较近、信噪比(Signal Noise Ratio,SNR)较低和快拍数较小的情况下可以更好地估计波达方向,且具备更高的分辨率和精度。
