Now showing items 1-6 of 6
Dynamic Range, Stability, and Fault-tolerant Capability of Finite-precision RLS Systolic Array Based on Givens Rotations
The QRD RLS algorithm is generally recognized as having good numerical properties under finite-precision implementation. Also, it is very suitable for VLSI implementation since it can be easily mapped onto a systolic array. ...
Fast Orthogonalization Algorithm and Parallel Implementation for AR Spectral Estimation Based on Forward-Backward Linear Prediction
High-resolution spectral estimation is an important subject in many applications of modern signal processing. The fundamental problem in applying various high-resolution spectral estimation algorithms is the computational ...
Systolic Implementations of Up/Down-dating Cholesky Factorization Using Vectorized Gram-Schmidt Pseudo Orthogonalization
We propose a new class of hyperbolic Gram-Schmidt methods to simultaneously update and downdate the Cholesky factor of a sample covariance matrix efficiently with applications to sliding window recursive least squares (RLS) ...
VLSI Algorithms and Architectures for Complex Householder Transformation with Applications to Array Processing
The Householder transformation is considered to be desirable among various unitary transformations due to its superior computational efficiency and robust numerical stability. Specifically, the Householder transformation ...
A Unified Approach for QRD-Based Recursive Least-Squres Estimation without Square Roots
The QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective towards adaptive signal processing in modern communications, radar, and sonar systems implementable with ...
Systolic Block Householder Transformation for RLS Algorithm with Two-level Pipelined Implementation
The QRD RLS algorithm is one of the most promising RLS algorithms, due to its robust numerical stability and suitability for VLSI implementation based on a systolic array architecture. Up to now, among many techniques to ...