Showing papers on "Recursive least squares filter published in 2008"

Diffusion recursive least-squares for distributed estimation over adaptive networks

Abstract: We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus, requiring a large amount of energy for communication. Incremental strategies that obtain the global solution have been proposed, but they require the definition of a cycle through the network. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution. We also show how to select the combination weights optimally.

A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification

Abstract: The performance of the recursive least-squares (RLS) algorithm is governed by the forgetting factor. This parameter leads to a compromise between (1) the tracking capabilities and (2) the misadjustment and stability. In this letter, a variable forgetting factor RLS (VFF-RLS) algorithm is proposed for system identification. In general, the output of the unknown system is corrupted by a noise-like signal. This signal should be recovered in the error signal of the adaptive filter after this one converges to the true solution. This condition is used to control the value of the forgetting factor. The simulation results indicate the good performance and the robustness of the proposed algorithm.

Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

Abstract: This paper proposes a regularized robust recursive least squares (R3LS) method for online estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data. A robust objective function is utilized to reduce the negative influence from nontypical data, which include outliers and missing data. A dynamic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and nontypical measurement data.

Low-Complexity RLS Algorithms Using Dichotomous Coordinate Descent Iterations

Abstract: In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of operations per sample; being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm ( multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented.

Blind Adaptive Constrained Reduced-Rank Parameter Estimation Based on Constant Modulus Design for CDMA Interference Suppression

Abstract: This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems.

Online vehicle mass estimation using recursive least squares and supervisory data extraction

Abstract: This paper examines the online estimation of onroad vehicles' mass. It classifies existing estimators based on the dynamics they use for estimation and whether they are event-seeking or averaging. It then proposes an algorithm comparable to this literature in accuracy and speed, but unique in its minimal instrumentation needs and ability to provide conservative mass error estimates, in the 3sigma sense. The algorithm builds on the simple idea, inspired by perturbation theory, that inertial dynamics dominate vehicle motion over certain types of maneuvers. A supervisory algorithm searches for those maneuvers, and feeds the resulting filtered data into a recursive least squares-based mass estimator and conservative mass error estimator. Both simulation and field data demonstrate the viability of the resulting approach.

Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction

Abstract: An important strength of learning classifier systems (LCSs) lies in the combination of genetic optimization techniques with gradient-based approximation techniques. The chosen approximation technique develops locally optimal approximations, such as accurate classification estimates, Q-value predictions, or linear function approximations. The genetic optimization technique is designed to distribute these local approximations efficiently over the problem space. Together, the two components develop a distributed, locally optimized problem solution in the form of a population of expert rules, often called classifiers. In function approximation problems, the XCSF classifier system develops a problem solution in the form of overlapping, piecewise linear approximations. This paper shows that XCSF performance on function approximation problems additively benefits from: 1) improved representations; 2) improved genetic operators; and 3) improved approximation techniques. Additionally, this paper introduces a novel closest classifier matching mechanism for the efficient compaction of XCS's final problem solution. The resulting compaction mechanism can boil the population size down by 90% on average, while decreasing prediction accuracy only marginally. Performance evaluations show that the additional mechanisms enable XCSF to reliably, accurately, and compactly approximate even seven dimensional functions. Performance comparisons with other, heuristic function approximation techniques show that XCSF yields competitive or even superior noise-robust performance.

A new wristband wearable sensor using adaptive reduction filter to reduce motion artifact

Abstract: Reduction the motion artifact from photo plethysmograph (PPG) signals can improve the precision of the measurement of heart rate or arterial oxygen saturation in movement state. This paper presents a new wristband PPG/ACC sensor device, which utilizes the adaptive filter technique to reduce the motion artifact of the original PPG signals. A 3-axis MEMS accelerometer sensor is attached to the PPG sensor to detect the wrist movement. A fast transversal RLS algorithm is performed to reduce the computed complexity of the adaptive filter. Experiment results show that this device can provide more reliable and stable signals against motion artifact corruption under the typical types of movement, such as swinging arms.

Geometry Control of the Deposited Layer in a Nonplanar Laser Cladding Process Using a Variable Structure Controller

Abstract: This paper presents a closed-loop laser cladding process used in nonplanar deposition of desired metallic materials. In the proposed system, the deposited layer geometry is continuously controlled via a sliding mode controller (SMC). The controller, which uses the scanning speed as the control input, is designed based on a parametric Hammerstein model. The model is a parametric dynamic model with several unknown parameters, which are identified experimentally using the recursive least squares method. The designed SMC is robust to all model parameters' uncertainties and disturbances. The results showed that the tracking accuracy improves and the chattering effect reduces if an integrator on the scanning speed is added to the controller. It was observed that this addition decreases the response speed. The performance of the proposed controllers was verified through the fabrication of several parts made of SS303-L. This verification indicates that the developed closed-loop laser cladding process can reduce stair-step effects as well as production time in rapid prototyping of functional parts created with the adaptive slicing technique.

The residual based extended least squares identification method for dual-rate systems

Abstract: In this paper, we focus on a class of dual-rate sampled-data systems in which all the inputs u(t) are available at each instant while only scarce outputs y(qt) can be measured (q being an integer more than unity). To estimate the parameters of such dual-rate systems, we derive a mathematical model by using the polynomial transformation technique, and apply the extended least squares algorithm to identify the dual-rate systems directly from the available input-output data {u(t),y(qt)}. Then, we study the convergence properties of the algorithm in details. Finally, we give an example to test and illustrate the algorithm involved.

Lms and rls algorithms for smart antennas in a w-cdma mobile communication environment

Abstract: Wireless mobile communication systems will be more sophisticated and wide spread in future. This growth demands not only for capacity but also high quality of service and better coverage without increase in radio frequency spectrum allocated for mobile applications. Wireless systems used fixed antenna systems in the past, but space division multiple access systems use smart antennas. These smart antennas dynamically adapt to changing traffic requirements. Smart antennas are usually employed at the base station and radiate narrow beams to serve different users. The complex weight computations based on different criteria are incorporated in the signal processor in the form of software algorithms. This article focuses on adaptive beam forming approach based on smart antennas and adaptive algorithms used to compute the complex weights like Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms.

Unified approach to adaptive filters and their performance

Abstract: A streamlined theory is presented for adaptive filters within which the major adaptive filter algorithms can be seen as special cases. The algorithm development part of the theory involves three ingredients: a preconditioned Wiener Hopf equation, its simplest possible iterative solution through the Richardson iteration, and an estimation strategy for the autocorrelation matrix, the cross-correlation vector and a preconditioning matrix. This results in a generalised adaptive filter in which intuitively plausible parameter selections give the major adaptive filter algorithms as special cases. This provides a setting where the similarities and differences between the many different adaptive filter algorithms are clearly and explicitly exposed. Based on the authors' generalised adaptive filter, expressions for the learning curve, the excess mean square error and the mean square coefficient deviation are developed. These are general performance results that are directly applicable to the major families of adaptive filter algorithms through the selection of a few parameters. Finally, the authors demonstrate through simulations that these results are useful in predicting adaptive filter performance.

A Nonlinear Acoustic Echo Canceller Using Sigmoid Transform in Conjunction With RLS Algorithm

Abstract: Nonlinearity of amplifiers and/or loudspeakers gives rise to nonlinear echo in acoustic systems, which seriously degrades the performance of speech and audio communications. Many nonlinear acoustic echo cancellation (AEC) methods have been proposed. In this paper, a simple yet efficient nonlinear echo cancellation scheme is presented by using an adaptable sigmoid function in conjunction with a conventional transversal adaptive filter. The new scheme uses the least mean square (LMS) algorithm to update the parameters of sigmoid function and the recursive least square (RLS) algorithm to determine the coefficient vector of the transversal filter. The proposed AEC is proved to be convergent under some mild assumptions. Computer simulations show that the proposed scheme gives a superior echo cancellation performance over the well known Volterra filter approach when the echo path suffers from the saturation-type nonlinear distortion. More importantly, the new AEC has a much lower computational complexity than the Volterra-filter-based method.

The Applications and Simulation of Adaptive Filter in Noise Canceling

Abstract: In practical application, the statistical characteristics of signal and noise are usually unknown or can't have been learned so that we hardly design fix coefficient digital filter. In allusion to this problem, the theory of the adaptive filter and adaptive noise cancellation are researched deeply in the thesis. According to the LMS and the RLS algorithms realize the design and simulation of adaptive algorithms in noise canceling, and compare and analyze the result then prove the advantage and disadvantage of two algorithms .We simulates the adaptive filter with MATLAB, the results prove its performance is better than the use of a fixed filter designed by conventional methods.

Real-time system identification for load monitoring and transient handling of Dc-Dc supplies

Abstract: Switching mode power supply (SMPS) behavior depends entirely upon known component values and often unknown load impedances Load change produces voltage over- and undershoots and a design-point shift In most cases, the controller is blind to these shifts However, load knowledge is an essential design parameter and should precede optimal and adaptive control techniques System identification algorithms implemented on digital processors open new opportunities in control and system performance Typical studies in SMPS identification are based on offline steady-state measurements that form nonparametric frequency-domain models This paper investigates real-time system identification algorithms that generate parametric models - a practical form for pole-placement and root-locus design Two key metrics are introduced: parameter error and convergence time to describe algorithm accuracy and speed, respectively, after abrupt parameter changes - a common SMPS load scenario Hardware and simulation results will show that an algorithm called recursive least squares, in its most basic form, could reasonable approximate the input-to-inductor current plant for static loads during startup, but not for abrupt load steps

Modeling of rate-dependent hysteresis in piezoelectric actuators

Abstract: A method for the identification of rate-dependent hysteresis inherent in piezoelectric actuators is proposed. A hysteretic operator is proposed to specify the change tendency of hysteresis. Then, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. Based on this expanded input space, a non-linear auto-regressive moving average model with exogenous inputs (NARMAX) is utilized to describe the hysteresis. Both the modified Akaikepsilas information criterion (MAIC) and the recursive least squares (RLS) algorithm are employed to estimate the appropriate structure and coefficients of the model. Finally, the experimental results on the modeling of hysteresis existing in a piezoelectric actuator are presented.

Online adaptive utilization control for real-time embedded multiprocessor systems

Abstract: To provide Quality of Service (QoS) guarantees in open and unpredictable environments, the utilization control problem is defined to keep the processor utilization at the schedulable utilization bound, even in the face of unpredictable and/or varying task execution times To handle the end-to-end task model where each task is comprised of a chain of subtasks distributed on multiprocessors, researchers have used Model Predictive Control (MPC) to address the Multiple-Input, Multiple-Output (MIMO) control problem Although MPC can handle a limited range of model uncertainties due to execution time estimation errors, the system may suffer performance deterioration or even become unstable if the actual task execution times are much larger than their estimated values In this paper, we present an online adaptive optimal control approach using Recursive Least Squares (RLS) based model estimator plus Linear Quadratic (LQ) optimal controller We use simulation experiments to demonstrate the effectiveness of our controller compared with the MPC-based controller

Extraction of fetal ECG using adaptive Volterra filters

Abstract: In this paper we present a new method for extracting the fetal electrocardiogram (FECG) signal from one thoracic ECG signal and one or more abdominal signals. Our method is based on the use of an adaptive Volterra filter (AVF) that is capable of synthesizing the nonlinear relation between the mother thoracic ECG signal and the abdominal signals which contains a transformed mother ECG, the fetal ECG and other noise elements. An adaptive multi-sensory noise canceler structure is adopted for the extraction purpose. In the case where more than one abdominal signals are used, the proposed algorithm uses a linear combiner (LC) to form a primary signal from those abdominal signals. The LC and the AVF are updated by the RLS algorithm. The proposed method is applied to real ECG measurements to demonstrate its superior effectiveness.

Performance of RLMS algorithm in adaptive array beam forming

Abstract: This paper examines the performance of an adaptive linear array employing the new RLMS algorithm, which consists of a recursive least square (RLS) section followed by a least mean square (LMS) section. The performance measures used are output and input signal-to-interference plus noise ratios (SINR), side lobe level (SLL), and ?SINR0 as a function of the direction of arrival of the interfering signal. Computer simulation results show that the performance of RLMS is superior to either the RLS or LMS based on these measures, particularly when operating with low input SINR.

Structural Estimation of Real Options Models

Abstract: We propose a numerical approach for structural estimation of a class of Discrete (Markov) Decision Processes emerging in real options applications. The approach is specifically designed to account for two typical features of aggregate data sets in real options: the endogeneity of firms' decisions; the unobserved heterogeneity of firms. The approach extends the Nested Fixed Point algorithm by Rust (1987,1988) because both the nested optimization algorithm and the integration over the distribution of the unobserved heterogeneity are accommodated using a simulation method based on a polynomial approximation of the value function and on recursive least squares estimation of the coefficients. The Monte Carlo study shows that omitting unobserved heterogeneity produces a significant estimation bias because the model can be highly non-linear with respect to the parameters.

Extraction of Fetal ECG Signal via Adaptive Noise Cancellation Approach

Abstract: The non-invasive fetal Electrocardiogram (FECG) can provide reliable information about the fetus'state The original FECG signal is nevertheless very complex and severely contaminated by external disturbances or noises It is very hard, even not impossible, to reliably extract the FECG from the abdominal signal using traditional techniques In this work the recursive least squares (RLS) based adaptive noise canceling (ANC) approach is applied to eliminate the maternal ECG (MECG) and hence to extract the FECG The experimental results have demonstrated that the developed ANC approach can speed up convergence of the normalized least mean squares (NLMS) algorithm and is able to track non-stationary FECG signal in an adaptive manner