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New Improved recursive least-squares Adaptive-Filtering algorithms

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2013年第6期60卷 1548-1558页

作者： Bhotto, Md Zulfiquar Ali Antoniou, Andreas Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 3P6 Canada

Two new improved recursive least-squares adaptive- filtering algorithms, one with a variable forgetting factor and the other with a variable convergence factor are proposed. Optimal forgetting and convergence factors are obtained by minimizing the mean square of the noise-free a posteriori error signal. The determination of the optimal forgetting and convergence factors requires information about the noise-free a priori error which is obtained by solving a known L-1 - L-2 minimization problem. Simulation results in system-identification and channel-equalization applications are presented which demonstrate that improved steady-state misalignment, tracking capability, and readaptation can be achieved relative to those in some state-of-the-art competing algorithms.

关键词： Adaptive filters adaptive-filtering algorithms recursive least-squares algorithms forgetting factor convergence factor

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Numerically stable fast convergence least-squares algorithms for multichannel active sound cancellation systems and sound deconvolution systems

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SIGNAL PROCESSING 2002年第5期82卷 721-736页

作者： Bouchard, M Univ Ottawa Sch Informat Technol & Engn Ottawa ON K1N 6N5 Canada

In recent years, recursive least-squares (RLS) algorithms and fast-transversal-filters (FTF) algorithms have been introduced for multichannel active sound cancellation (ASC) systems and multichannel sound deconvolution (MSD) systems. It was reported that these algorithms can greatly improve the convergence speed of the ASC/MSD systems using adaptive FIR filters. However, numerical instability of the algorithms is an issue that needs to be resolved. In this paper, extensions of numerically stable realisations of RLS algorithms such as the inverse QR-RLS, the QR decomposition least-squares-lattice (QRD-LSL) and the symmetry preserving RLS algorithms are introduced for the specific problem of multichannel ASC/MSD. Multichannel versions of some of these algorithms have previously been published for prediction or identification systems, but not for control systems. The case of underdetermined ASC/MSD systems (i.e. systems with more actuators than error sensors) is also considered, to show that in these cases it may be required to use constrained algorithms in order to have numerical stability. Constrained algorithms for multichannel ASC/MSD systems are therefore introduced for two types of constraints: minimisation of the actuator signals power and minimization of the adaptive filters square coefficients. Simulation results are shown to verify the numerical stability of the algorithms introduced in the paper. circle * 2002 Elsevier Science B.V. All rights reserved.

关键词： active sound cancellation active noise control sound deconvolution multichannel sound reproduction constrained least-squares algorithms recursive least-squares algorithms QR-RLS QRD-LSL

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Fault-tolerant recursive least-squares computations on a mesh-connected parallel processor

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2002年第7期62卷 1142-1167页

作者： Zomaya, AY Yates, A Olariu, S Univ Western Australia Dept Elect & Elect Engn Parallel Comp Res Lab Perth WA 6907 Australia

recursive least squares (RLS) is a popular iterative method used for the modeling of systems while in operation RLS provides an estimate for unknown parameters of a system based on some known parameters and inputs and out,puts of that system. This technique is used frequently in digital signal processing and control applications. where it is not possible to completely determine the current state of the system. The RLS procedure incurs intensive computations in every iteration of the algorithm. To implement RLS in situ at a reasonable sampling rate. the complexity of the system's model must be reduced, or the available computing power must be increased. This paper examines methods for increasing the computing power by implementing RLS algorithms on a parallel processing platform While there has been a large body of research on using parallel processors for the computation of adaptive algorithms, little of this research has examined fault tolerant aspects'. As fault tolerance is a critical aspect of any real-time system. this work will examine some factors that should be considered when implementing a real-time adaptive algorithm oil a parallel processor system, (C) 20002 Elsevier Science (USA).

关键词： fault-tolerant computations mesh architectures recursive least-squares algorithms parallel processing

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Multi-band decomposition of the linear prediction error applied to adaptive AR spectral estimation

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1997年第2期E80A卷 365-376页

作者： Resende, FGV Tokuda, K Kaneko, M Nishihara, A Department of Physical Electronics Tokyo Institute of Technology Tokyo 152 Japan

A new structure for adaptive AR spectral estimation based on multi-band decomposition of the linear prediction error is introduced and the mathematical background for the solution of the related adaptive filtering problem is derived. The presented structure gives rise to AR spectral estimates that represent the true underlying spectrum with better fidelity than conventional LS methods by allowing an arbitrary trade-off between variance of spectral estimates and tracking ability of the estimator along the frequency spectrum. The linear prediction error is decomposed through a filter bank and components of each band are analyzed by different window lengths, allowing long windows to track slowly varying signals and short windows to observe fastly varying components. The correlation matrix of the input signal is shown to satisfy both time-update and order-update properties for rectangular windowing functions, and an RLS algorithm based on each property is presented. Adaptive forward and backward relations are used to derive a mathematical framework that serves as a basis for the design of fast RLS algorithms. Also, computer experiments comparing the performance of conventional and the proposed multi-band methods are depicted and discussed.

关键词： digital signal processing AR spectral estimation filter banks adaptive filtering recursive least-squares algorithms

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Adaptive AR spectral estimation based on wavelet decomposition of the linear prediction error

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1996年第5期E79A卷 665-673页

作者： Resende, FGV Tokuda, K Kaneko, M Faculty of Engineering Tokyo Institute of Technology Tokyo 152 Japan Department of Intelligence and Computer Science Nagoya Institute of Technology Nagoyashi 466 Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa-ken 923-12 Japan

A new adaptive AR spectral estimation method is proposed. While conventional least-squares methods use a single windowing function to analyze the linear prediction error, the proposed method uses a different window for each frequency band of the linear prediction error to define a cost function to be minimized. With this approach, since time and frequency resolutions can be traded off throughout the frequency spectrum, an improvement on the precision of the estimates is achieved. In this paper, a wavelet-like time-frequency resolution grid is used so that low-frequency components of the linear prediction error are analyzed through long windows and high-frequency components are analyzed through short ones. To solve the optimization problem for the new cost function, special properties of the correlation matrix are used to derive an RLS algorithm on the order of M(2), where M is the number of parameters of the AR model. Computer simulations comparing the performance of conventional RLS and the proposed methods are shown. In particular, it can be observed that the wavelet-based spectral estimation method gives fine frequency resolution at low frequencies and sharp time resolution at high frequencies, while with conventional methods it is possible to obtain only one of these characteristics.

关键词： digital signal processing spectral estimation wavelet theory adaptive filtering recursive least-squares algorithms

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