This paper solves the problem of designing recursive-least-squares (RLS) lattice (or order-recursive) algorithms for adaptive filters that do not involve tapped-delay-line structures. In particular, an RLS–Laguerre lattice filter is obtained.

This paper addresses a new design method of recursive least-squares (RLS) and finite impulse response (FIR) filter, using covariance information, in linear continuous-time stochastic systems. The signal process is observed with additive white noise. It is assumed that the white observation noise is independent of the signal process. The auto-covariance function of the signal is expressed in the...

Within the context of recursive least-squares, the implementation of a Householder algorithm for block updating the QR decomposition, on massively parallel SIMD systems, is considered. Initially, two implementations based on di€erent mapping strategies for distributing the data matrices over the processing elements of the parallel computer are investigated. Timing models show that neither of th...

The existing derivations of conventional fast RLS adaptive filters are intrinsically dependent on the shift structure in the input regression vectors. This structure arises when a tapped-delay line (FIR) filter is used as a modeling filter. In this paper, we show, unlike what original derivations may suggest, that fast fixed-order RLS adaptive algorithms are not limited to FIR filter structures...

The control of the hopping height in a one-legged machine is studied. The general aim is to decrease sensitivity of the hopping height to drifts in the machine’s parameters. The proposed approach combines a near-inverse controller which uses height feedback, with a recursive least-squares parameter estimator which continually tunes the controller. The paper presents the mechanical design of the...

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We ...

New expressions are derived for the mean weight misadjustment in the recursive least squares (RLS) algorithm for first-order Markov channel estimation. The expressions derived are general in that they take into account the correlation in the input. It is sbown that the additive system noise is amplified by a correlation amplification factor that is defined as a function of the input autocorrela...

This paper is devoted to the stochastic analysis of recursive least squares (RLS) identification algorithms with an exponential forgetting factor. A persistent excitation assumption of a conditional type is made that does not prevent the regressors from being a dependent sequence. Moreover, the system parameter is modeled as the output of a random-walk type equation without extra constraints on...

Adaptive algorithm based on multi-channel linear prediction is an effective dereverberation method balancing well between the attenuation of the long-term reverberation and the dereverberated speech quality. However, the abrupt change of the speech source position, usually caused by the shift of the speakers, forms an obstacle to the adaptive algorithm and makes it difficult to guarantee both t...

The recurrent least squares (RLS) learning approach is proposed for controlling the learning rate in parallel principal subspace analysis (PSA) and in a wide class of principal component analysis (PCA) associated algorithms with a quasi{parallel extraction ability. The purpose is to provide a useful tool for applications where the learning process has to be repeated in an on{line self{adaptive ...