High Resolution Methods Based On Rank Revealing Triangular Factorizations

نویسندگان

  • M. Bouri
  • S. Bourennane
چکیده

In this paper, we propose a novel method for subspace estimation used high resolution method without eigendecomposition where the sample Cross-Spectral Matrix (CSM) is replaced by upper triangular matrix obtained from LU factorization. This novel method decreases the computational complexity. The method relies on a recently published result on Rank-Revealing LU (RRLU) factorization. Simulation results demonstrates that the new algorithm outperform the Householder rank-revealing QR (RRQR) factorization method and the MUSIC in the low Signal to Noise Ratio (SNR) scenarios. Keywords— Factorization, Localization, Matrix, Signal subspace.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Blocked rank-revealing QR factorizations: How randomized sampling can be used to avoid single-vector pivoting

Given a matrix A of size m × n, the manuscript describes a algorithm for computing a QR factorization AP = QR where P is a permutation matrix, Q is orthonormal, and R is upper triangular. The algorithm is blocked, to allow it to be implemented efficiently. The need for single vector pivoting in classical algorithms for computing QR factorizations is avoided by the use of randomized sampling to ...

متن کامل

Computing with functions in two dimensions

New numerical methods are proposed for computing with smooth scalar and vector valued functions of two variables defined on rectangular domains. Functions are approximated to essentially machine precision by an iterative variant of Gaussian elimination that constructs near-optimal low rank approximations. Operations such as integration, differentiation, and function evaluation are particularly ...

متن کامل

Algorithms for Approximate Subtropical Matrix Factorization

Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra – and the summation of the rank-1 components to build the approximation of the original ma...

متن کامل

Multi-layer Hierarchical Structures and Factorizations

We propose multi-layer hierarchically semiseparable (MHS) structures for the fast factorizations of dense matrices arising from multi-dimensional discretized problems such as certain integral operators. The MHS framework extends hierarchically semiseparable (HSS) forms (which are essentially one dimensional) to higher dimensions via the integration of multiple layers of structures, i.e., struct...

متن کامل

A robust inner-outer HSS preconditioner

This paper presents an inner-outer preconditioner for symmetric positive definite matrices based on hierarchically semiseparable (HSS) matrix representation. A sequence of new HSS algorithms are developed, including some ULV-type HSS methods and triangular HSS methods. During the construction of this preconditioner, off-diagonal blocks are compressed in parallel, and an approximate HSS form is ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009