Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

QRD - RLS Adaptive Filtering

The main limitation of FQRD-RLS algorithms is that they lack an explicit weight vector term. Furthermore, they do not directly provide the variables allowing for a straightforward computation of the weight vector as is the case with the conventional QRD-RLS algorithm, where a back-substitution procedure can be used to compute the coefficients. Therefore, the applications of the FQRD-RLS algorit...

Using a fast RLS adaptive algorithm for efficient speech processing

In this paper, a new method is presented that offers efficient computation of Linear Prediction Coefficients (LPC) via a new Recursive Least Squares (RLS) adaptive filtering algorithm. This method can be successfully used in speech coding and processing. The introduced algorithm is numerically robust, fast, parallelizable and has particularly good tracking properties. By means of this scheme, L...

An inverse QRD-RLS algorithm for linearly constrained minimum variance adaptive filtering

In this paper an inverse QR decomposition based recursive least-squares algorithm for linearly constrained minimum variance filtering is proposed. The proposed algorithm is numerically stable in finite precision environments and is suitable for implementation in systolic arrays or DSP vector architectures. Its performance is illustrated by simulations of a blind receiver for a multicarrier CDMA...

Exact RLS Laguerre-lattice adaptive filtering

Spectral line RLS adaptive filtering algorithm

A family of adaptive filtering algorithms for processing signals which have energy concentrated in a relatively small number of component subspaces in the spectral domain is introduced. The approach is based on transform domain signal decomposition and linear least squares filtering of the selected subset of transform domain signal components. The derivation is based on the linear least squares...