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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. ...

Tufts and Kumaresan (1982) first proposed using a SVD-based method to solve the forward-backward linear prediction (FBLP) least-squares problem for resolving closely spaced frequencies of multiple sinusoids from limited ...

We propose a dual-state systolic structure to perform joint up/down-dating operations encountered in windowed recursive least squares (RLS) estimation problems. It is derived by successively performing Givens rotations for ...

In this paper, we propose two multi-phase systolic algorithms to solve the spectral decomposition problem based on the QR algorithm. The spectral decomposition is one of the most computationally intensive modern signal ...

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) ...

In this paper, we propose a new algorithm-based fault-tolerant method derived from the inherent nature of the QR lease-squares systolic algorithm. Since the residuals of different desired responses can be computed ...

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 ...

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 ...

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 ...