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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： E. Fernandez-Gaucherand J. Cruz School of Electrical Engineering and Computer Science University of Oklahama Norman OK USA

The performances of two low-complexity IIR adaptive filtering algorithms applied to the equalization of a typical transmission line channel are compared. The algorithms considered are the Simple Hyperstable Adaptive Recursive Filter (SHARF) and the stochastic gradient equation error algorithm. Gradient descent techniques are shown to be ill suited for transmission line channels.

关键词： Adaptive equalizers Transmission lines Adaptive filters filtering algorithms Coaxial cables Signal processing algorithms Nonlinear filters Power cables Frequency Equations

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On unbiased adaptive IIR filtering algorithms

On unbiased adaptive IIR filtering algorithms

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： J.E. Cousseau P.D. Donate P.S.R. Diniz Departamento de Ing. Eléctrica Universidad Nacional del Sur Bahia Blanca Argentina Prog.de Engenharia Elétrica e Departmento.de Eletrônica Federal University of Rio de Janeiro (COPPE-EE) Rio de Janeiro Brazil

Some properties of a recently proposed unbiased criteria for adaptive IIR filtering, namely the Master-slave Expanded Numerator Algorithm (XNA), is investigated. The XNA algorithm combines the minimization of two criteria that are quadratic in the parameters. Although the XNA method achieves an unbiased behavior in sufficient-order system identification applications, it generates undesired stationary points. Through the study presented in this paper, explicit expressions for the unwanted stationary points can be obtained. By closely examining the obtained results, a new unbiased adaptive IIR filtering algorithm is presented. Using similar ideas to the Steiglitz-McBride method, the new algorithm is based on the minimization of an off-line error function. After the proposal of the new unbiased (off-line) algorithm, which is quadratic in the parameters, we present the analysis of the possible stationary points. Some examples are shown illustrating the performance of the new algorithm in a system identification application.

关键词： filtering algorithms System identification Adaptive filters Master-slave Samarium Minimization methods Noise reduction Decision feedback equalizers Robust stability Computational complexity

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On the nonlinear dynamics of fast filtering algorithms

On the nonlinear dynamics of fast filtering algorithms

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IEEE Conference on Decision and Control

作者： C.I. Byrnes A. Lindquist Y. Zhon Department of Systems Science and Mathematics Washington University Saint Louis MO USA Division of Optimization and Systems Theory Royal Institute of Technology Stockholm Sweden

A fundamental open problem in linear filtering and estimation is addressed, i.e. what is the steady-state or asymptotic behavior of the Kalman filter, or the Kalman gain, when the observed stationary stochastic process is not generated by a finite-dimensional stochastic system, or when it is generated by a stochastic system having higher dimensional unmodeled dynamics? For a scalar observation process, necessary and sufficient conditions are derived for the Kalman filter to converge, using methods from stochastic systems and from nonlinear dynamics, especially the use of stable, unstable and center manifolds. It is shown that, in nonconvergent cases, there exist periodic points of every period p, p>or=3 which are arbitrarily close to initial conditions having unbounded orbits. This rigorously demonstrates that the Kalman filter can also be sensitive to initial conditions.< >

关键词： filtering algorithms Nonlinear dynamical systems Stochastic systems Maximum likelihood detection Steady-state Kalman filters Stochastic processes Sufficient conditions Orbits

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A CLASS OF LEAST-SQUARES filtering AND IDENTIFICATION algorithms WITH SYSTOLIC ARRAY ARCHITECTURES

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IEEE TRANSACTIONS ON INFORMATION THEORY 1991年第1期37卷 43-52页

作者： KALSON, SZ YAO, K UNIV CALIF LOS ANGELES DEPT ELECT ENGNLOS ANGELESCA 90024

A unified approach is presented for deriving a large class of new and previously known time and order recursive least-squares algorithms with systolic array architectures, suitable for high throughput rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Both the growing and sliding memory, exponentially weighted least-squares criteria are considered.< >

关键词： filtering algorithms Systolic arrays Nonlinear filters Signal processing algorithms Wiener filter Adaptive arrays Array signal processing Radar signal processing System identification Adaptive equalizers

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Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room

Experimental comparison of particle filtering algorithms for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： E.A. Lehmann D.B. Ward R.C. Williamson Department of Telecommunications Engineering RSlSE Australian National University Canberra ACT Australia Department of Electrical & Electronic Engineering Imperial College London UK

Traditional acoustic source localization techniques attempt to determine the current location of an acoustic source from data obtained at an array of sensors during the current time only. Recently, state-space methods have been proposed that use particle filters to perform recursive estimation of the current source location using all previous data. In this paper we present an overview of these particle filter algorithms, and formulate performance measures for determining their ability to track a moving source. We present results of experiments using reverberant data recorded in a real room, and show that steered beamforming methods have improved performance over GCC-based approaches.

关键词： filtering algorithms Sensor arrays Particle filters Acoustic arrays Acoustic sensors State-space methods Recursive estimation Position measurement Particle measurements Particle tracking

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Adaptive filtering algorithms and alpha stable signals

Adaptive filtering algorithms and alpha stable signals

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： E. Masry Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

We consider the problem of optimal FIR linear and regression filters for jointly stationary symmetric alpha stable processes with index alpha greater than one. We then consider the signed-error and sign-sign adaptive ... 详细信息

关键词： Adaptive filters filtering algorithms Signal processing algorithms Random variables Convergence Finite impulse response filter Gaussian processes Vectors Nonlinear filters Maximum likelihood detection

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Systolic arrays for implementation of order-recursive least-squares adaptive filtering algorithms

Systolic arrays for implementation of order-recursive least-...

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Proceedings of the International Conference on Systolic Arrays

作者： F. Ling Codex Corporation Mansfield MA USA

Three systolic arrays suitable for implementation of order-recursive least-squares (ORLS) adaptive algorithms are considered. It is shown that they can be constructed using two types of elementary cells. A classification of systolic implementations of the ORLS adaptive algorithms is given by exploiting the possible variations of the elementary cells. The investigation of the array structures and variations of the elementary cells leads to a systematic approach to designing reconfigurable systolic arrays for implementation of ORLS algorithms. As an application of this approach, a novel least-squares (LS) lattice algorithm based on Givens rotations is derived.< >

关键词： Systolic arrays Adaptive filters filtering algorithms Lattices Adaptive algorithm Algorithm design and analysis Least squares approximation Throughput Hardware Matrix decomposition

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A new structure for adaptive signal processing with combined FIR & IIR filtering algorithms

A new structure for adaptive signal processing with combined...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J. Jiang R. Doraiswami Department of Electrical Engineering University of New Brunswick Canada

A new adaptive algorithm for system identification and adaptive signal processing has been proposed in this paper. The algorithm can be viewed as a combination of the output error type and the equation error type of identifier (i.e. a combination of IIR adaptive filter and FIR adaptive filter in signal processing application). It has been shown both mathematically and by computer simulation that the proposed algorithm can guarantee the globally asymptotical stability under certain regularity conditions on the distribution of signal modes. The transient behaviour of the algorithm is better than conventional output error type of identifier, especially in the case where the system model (signal model) has its eigenvalues on the unit circle. Under certain conditions on the system model (signal model), no moving average (MA) smoothing of the generalized error is needed for the strictly positive realness (SPR) conditions. It gives a reduced bias on the parameter estimate (compare to conventional adaptive FIR filter) when corresponding equation error is a bandlimited process.

关键词： Adaptive signal processing Finite impulse response filter filtering algorithms Signal processing algorithms Computer errors Equations Adaptive filters Adaptive algorithm System identification Application software

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Analysis of multiparameter optical sensor data with composite filtering algorithms

Analysis of multiparameter optical sensor data with composit...

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International Conference on Transparent Optical Networks

作者： R. Kasztelanic R. Kotynski H. Thienpont K. Chalasinska-Macukow Institute of Geophysics Department of Physics University of Warmia and Mazury Warsaw Poland Department of Applied Physics and Phofonics Vrije Universiteit Brussel Brussels Belgium

This paper presents a novel approach to the analysis of detection signals from optical sensors. The proposed technique is based on the pattern recognition algorithms that belong to the family of composite linear filters and is compared against a MAP-estimate approach. Our study is focused on the information recovery from the reflectivity spectrum of a Bragg sensor measured with an optical spectrum analyzer. However we note that the same scheme would be equally useful for the extraction of information obtained with a polarimetric fibre sensor or an optical chemical sensor, where in general the detection signal is multidimensional and includes data related to several measured parameters.

关键词： Algorithm design and analysis Optical sensors filtering algorithms Chemical sensors Signal detection Optical filters Optical fiber sensors Signal analysis Optical detectors Pattern recognition

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Beat tracking with particle filtering algorithms

Beat tracking with particle filtering algorithms

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

作者： S. Hainsworth M. Macleod Engineering Department Cambridge University Cambridge UK QinetiQ Malvern UK

The paper presents a beat tracking algorithm for musical audio signals. The method firstly extracts musical change points from the help signal and then uses a particle filtering algorithm to associate these to a tempo... 详细信息

关键词： Particle tracking filtering algorithms Power harmonic filters Frequency estimation Event detection Humans Application software Phase estimation Signal processing Foot

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