Toeplitz Inverse Eigenvalue Problem (ToIEP) and Random Matrix Theory (RMT) Support for Calculation of the Toeplitz Covariance Matrix Estimate

2023 IEEE 9TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, CAMSAP（2023）

引用 0|浏览1

暂无评分

摘要

"Toeplitization" or "redundancy averaging" is the well-known procedure of getting the Toeplitz matrix estimate from the standard sample covariance matrix. Despite the recently proven asymptotic consistency of this estimate (with...8,... 8,../.....), for the weakly positive definite covariance matrices, redundancy averaging typically leads to the estimates with a number of negative eigenvalues. In this paper, we demonstrate how these estimates may be rectified to meet a sub-optimal condition, using computational tools from ToIEP and RMT methodologies.

查看译文

关键词

Covariance Matrix,Random Matrix Theory,Toeplitz Covariance Matrix,Sample Covariance Matrix,Number Of Eigenvalues,Toeplitz Matrix,Standardized Covariance,Convergence Rate,Sample Matrix,Iterative Procedure,Variate,Hermitian Matrix,Spectral Norm,Signal Subspace,True Phase,Noise Subspace

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要