Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.

A generalized multichannel least squares lattice algorithm based on sequential processing stages

Abstract: A generalized multichannel least squares (LS) lattice algorithm which is appropriate for multichannel adaptive filtering and estimation is presented in this paper. It is shown that a muitichannel LS estimation algorithm with a different number of parameters to be estimated in each channel can be implemented by cascading lattice stages of nondescending dimension to form a generalized lattice structure. A new realization of a multichannel lattice stage is also presented. This realization employs only scalar operations and has a computational complexity of 0(p2) for each p-channel lattice stage.

Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

Abstract: A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.

Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers

Abstract: Noise, in the form of motion artifact, often leads to false information and acts as a limiting factor in the analysis of pulse oximetric signals. We propose an adaptive filter for filtering motion artifacts from pulse oximetry signals, with accelerometer signals as noise references. We study two adaptive filtering schemes: (1) single-axis and (2) dual-axes stress tests; and apply both the LMS and RLS algorithms to each scheme to compare their effectiveness. Results show that a single-axis adaptive filter employing the RLS algorithm (N=32 and /spl lambda/=0.9999) is adequate to minimize motion artifact.

Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

Abstract: This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.

A Sliding-Window Kernel RLS Algorithm and Its Application to Nonlinear Channel Identification

Abstract: In this paper we propose a new kernel-based version of the recursive least-squares (RLS) algorithm for fast adaptive nonlinear filtering. Unlike other previous approaches, we combine a sliding-window approach (to fix the dimensions of the kernel matrix) with conventional L2-norm regularization (to improve generalization). The proposed kernel RLS algorithm is applied to a nonlinear channel identification problem (specifically, a linear filter followed by a memoryless nonlinearity), which typically appears in satellite communications or digital magnetic recording systems. We show that the proposed algorithm is able to operate in a time-varying environment and tracks abrupt changes in either the linear filter or the nonlinearity.