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Gradient flow approach to discrete-time envelope-constrained filter design via orthonormal filters

IEE PROCEEDINGS-VISION IMAGE AND signal processing 2000年第1期147卷 79-88页

作者： Tseng, CH Teo, KL Zang, Z Cantoni, A Curtin Univ Technol Australian Commun Res Inst GPO Box U 1987 Perth WA 6845 Australia Hong Kong Polytech Univ Dept Math Appl Hong Kong Peoples R China

Using digital orthonormal filters and Lagrangian duality theory, the envelope-constrained (EC) filtering problem has been formulated as a dual quadratic programming (QP) problem with simple constraints. Applying the barrier-gradient and barrier-Newton methods based on the space transformation and gradient flow technique, two efficient design algorithms are constructed for solving this QP problem. An adaptive algorithm, based on the barrier-gradient algorithm, is developed to solve the EC filtering problem in a stochastic environment. The convergence properties are established in the mean and mean square error senses. To demonstrate the effectiveness of the proposed algorithms, a practical example using the Laguerre networks is solved for both the deterministic and stochastic cases.

关键词： Lagrangian duality theory adaptive filters quadratic programming convergence properties adaptive algorithm barrier-gradient method space transformation deterministic case stochastic processes Optimisation techniques design algorithms mean error sense digital filters mean square error sense Newton method digital orthonormal filters filtering methods in signal processing Interpolation and function approximation (numerical analysis) dual quadratic programming problem stochastic environment discrete-time envelope-constrained filter design Other topics in statistics discrete time filters orthonormal filters barrier-Newton method convergence of numerical methods Laguerre networks simple constraints gradient flow approach signal processing theory

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FIR filter design over discrete coefficients and least square error

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IEE PROCEEDINGS-VISION IMAGE AND signal processing 2000年第6期147卷 543-548页

作者： Dam, HH Nordebo, S Teo, KL Cantoni, A Curtin Univ Technol Australian Telecommun Res Inst Perth WA 6845 Australia Univ Karlskrona Ronneby Dept Signal Proc S-37225 Ronneby Sweden Hong Kong Pyrotech Univ Dept Appl Math Kowloon Hong Kong Peoples R China Univ Western Australia Dept Elect & Elect Engn Nedlands WA 6907 Australia

The difference routing digital filter (DRDF) consists of an FIR filter followed by a first-order integrator. This structure with power-of-two coefficients has been studied as a means of achieving low complexity, high sampling rate filters which can be implemented efficiently in hardware. The optimisation of the coefficients has previously been based on a time-domain least-squares error criterion. A new design method is proposed that includes a frequency-domain least-squares criterion with arbitrary frequency weighting and an improved method for handling quantisation of the filter coefficients. Simulation studies show that the new approach yields an improvement of up to 7 dB over existing methods and that oversampling can be used to improve performance.

关键词： optimisation computational complexity Digital signal processing frequency-domain least-squares criterion low complexity, high sampling rate filters Optimisation techniques difference routing digital filter first-order integrator FIR filters oversampling filtering methods in signal processing Interpolation and function approximation (numerical analysis) discrete coefficients power-of-two coefficients time-domain synthesis DRDF FIR filter design circuit optimisation least square error least squares approximations filter coefficients frequency weighting signal sampling Digital filters quantisation

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Restricted-structure linear estimators for multiple-model systems

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IEE PROCEEDINGS-VISION IMAGE AND signal processing 2000年第3期147卷 193-204页

作者： Grimble, MJ Univ Strathclyde Ind Control Ctr Glasgow G1 1QE Lanark Scotland

A new class of discrete-time optimal linear estimators is introduced for multiple model systems that minimises a minimum-variance criterion but where the structure is prespecified to have a simple low-order form. The restricted-structure estimator can be of much lower order than a Kalman or Wiener estimator and it minimises the estimation-error variance, subject to the constraint referred to. The numerical optimisation algorithm is simple to implement and full-order optimal solutions are available as a by-product of the analysis. The algorithm enables low-order optimal estimators to be computed that directly minimise the cost index across a set of possible linear signal or noise source models. The main technical advances lie in the theoretical analysis that enables the expanded cost expression to be simplified before the numerical solution is obtained, and the extension of the restricted-structure optimisation technique to multiple-model systems.

关键词： signal processing adaptive estimation restricted-structure optimisation optimisation adaptive estimator numerical optimisation algorithm Simulation, modelling and identification rolling mill Optimisation techniques parameter estimation minimum-variance criterion Wiener filters estimation-error variance noise source models Kalman estimator linear signal models numerical solution filtering methods in signal processing frequency-domain approach Kalman filters multiple-model systems optimal linear filtering X-ray gauge measurements Wiener estimator low-order structure cost index minimisation expanded cost expression discrete-time optimal linear estimators full-order optimal solutions restricted-structure linear estimators low-order optimal estimators signal processing theory

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Curvature and torsion feature extraction from freeform 3-D meshes at multiple scales

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IEE PROCEEDINGS-VISION IMAGE AND signal processing 2000年第5期147卷 454-462页

作者： Yuen, P Mokhtarian, F Khalili, N Illingworth, J Univ Surrey Ctr Vis Speech & Signal Proc Sch Elect Engn Informat Technol & Math Guildford GU2 5XH Surrey England

A novel technique is presented for multi-scale curvature computation on a smoothed 3-D surface. This is achieved by convolving local parameterisations of the surface iteratively with 2-D Gaussian filters. In the technique, each vertex of the mesh becomes a local origin around which semi-geodesic co-ordinates are constructed. A geodesic from the origin is first constructed in an arbitrary direction, typically the direction of one of the incident edges. The smoothing eliminates surface noise and slowly erodes small surface detail, resulting in gradual simplification of the object shape. The surface Gaussian and mean curvature values are estimated accurately at multiple scales, together with curvature zero-crossing contours. For better visualisation, the curvature values are then mapped to colours and displayed directly on the surface. Furthermore local maxima of Gaussian and mean curvatures, as well as the torsion maxima of the zero-crossing contours of Gaussian and mean curvatures are also located and displayed on the surface. These features can be utilised by later processes for robust surface matching and object recognition. The technique is independent of the underlying triangulation and is more efficient than volumetric diffusion techniques since 2-D rather than 3-D convolutions are employed. Another advantage is that it is applicable to incomplete surfaces which arise during occlusion or to surfaces with holes.

关键词： robust surface matching triangulation multiple scales Digital signal processing 2D convolution incomplete surfaces surface noise elimination local origin semi-geodesic co-ordinates feature extraction convolution occlusion image matching two-dimensional digital filters filtering methods in signal processing mean curvature value local parameterisations convolution 2D Gaussian filters free-form 3D meshes multi-scale curvature computation Image recognition smoothed 3D surface curvature zero-crossing contours Gaussian curvature value torsion feature extraction object recognition smoothing methods torsion maxima

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Multichannel fusion of subsurface radar images at different resolutions

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2000年第3期147卷 121-133页

作者： Lombardo, P Magenta, AM Pettinelli, E Univ Rome La Sapienza INFO COM Dept I-00184 Rome Italy Univ Rome La Sapienza Dept Elect Engn I-00184 Rome Italy

The authors deal with the fusion of multichannel and multiresolution subsurface radar images collected by different frequency channels. Since each radar frequency is most suited for the investigation of a specific range of depths, an accurate analysis of the subsurface over an extended depth interval requires images at different channels. By exploiting the physical model of EM pulse propagation in a stratified medium, three multichannel Wiener filters are derived for estimation of the subsurface impulse response from a set of radar surveys collected at different frequencies. By properly combining the echoes received at the different channels, the filters yield the high resolution of the high-frequency channels for the shallow interfaces and the good SNR of the low-frequency channels for the deep interfaces. A complete theoretical as well as simulated performance analysis is presented for the proposed filters. Moreover, the fusion techniques are applied to real data collected at two sites: a homogeneous subsurface with a number of embedded, known, finite objects and a simple stratified natural terrain. The former is used to assess the proper operation of the multichannel deconvolution, while the latter is used to show that the desired optimal performance is achieved for the interfaces at different depths.

关键词： electromagnetic wave propagation multichannel Wiener filters radar resolution Radar theory deep interfaces low-frequency channels simulated performance analysis SNR Wiener filters channel bank filters real data Electromagnetic wave propagation subsurface impulse response high-frequency channels homogeneous subsurface finite objects inhomogeneous media image resolution filtering methods in signal processing sensor fusion radar frequency optimal performance multichannel subsurface radar image stratified natural terrain echoes radar surveys Optical, image and video signal processing extended depth interval radar applications subsurface radar images radar imaging stratified medium high resolution transient response filtering theory multichannel deconvolution multiresolution subsurface radar image multichannel fusion deconvolution shallow interfaces electromagnetic pulse EM pulse propagation physical model

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Nonlinear RLS algorithm for amplitude estimation in class A noise

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IEE PROCEEDINGS-COMMUNICATIONS 2000年第2期147卷 81-86页

作者： Weng, JF Leung, SH City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

An adaptive nonlinear recursive least square (RLS) algorithm for amplitude estimation in class A noise is presented. For Gaussian input signal and class A noise, its mean and mean-square behaviours are studied. It is shown that the linear RLS and nonlinear RLS algorithm with the clipper function are stable in the mean and mean square. For non-Gaussian input, amplitude estimation in CDMA communication is presented. Simulation results show that the nonlinear RLS can provide good performance close to the Cramer-Rao bound and outperform the nonlinear LMS and the conventional RLS in impulse noise.

关键词： adaptive estimation Multiple access communication recursive estimation adaptive nonlinear recursive least square algorithm class A noise non-Gaussian input signal impulse noise CDMA communication nonlinear estimation filtering methods in signal processing Interpolation and function approximation (numerical analysis) mean behaviour Gaussian input signal Cramer-Rao bound code division multiple access filtering theory convergence of numerical methods least squares approximations RLS algorithm simulation results mean-square behaviour amplitude estimation clipper function

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Design of two-channel IIR filter banks with arbitrary group delay

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IEE PROCEEDINGS-VISION IMAGE AND signal processing 2000年第6期147卷 534-542页

作者： Lee, JH Niu, IC Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan

The nonlinear optimisation problem that results from considering the design of a two-channel nonuniform division filter bank is solved. This is through a frequency sampling and iterative approximation technique to find the tap coefficients and the reflection coefficients for the numerator and the denominator of the IIR analysis filters. An efficient stabilisation procedure ensures that the reflection coefficients lie in (-1,1). Simulation examples are provided for illustration.

关键词： IIR filters two-channel nonuniform division filter bank frequency sampling Digital signal processing delays circuit simulation Optimisation techniques channel bank filters group delay IIR analysis filters filtering methods in signal processing Interpolation and function approximation (numerical analysis) stabilisation procedure tap coefficients reflection coefficients denominator numerator iterative methods circuit optimisation two-channel IIR filter banks iterative approximation technique design signal sampling Digital filters nonlinear optimisation problem

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Impedance matrix compression using an effective quadrature filter

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IEE PROCEEDINGS-MICROWAVES ANTENNAS AND PROPAGATION 2000年第4期147卷 255-260页

作者： Huang, JM Leou, JL Jeng, SK Tarng, JH Natl Chiao Tung Univ Dept Commun Engn Hsinchu Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

An effective quadrature mirror filter (QMF) proposed by Vaidyanathan has been used to solve 2D scattering problems. QMF has been popular for some time in digital signal processing, under the names of multirate sampling, wavelets, etc. In this work, the impulse response coefficients of QMF were used to construct the wavelet transform matrix. Using the matrix to transform the impedance matrices of 2D scatterers produces highly sparse moment matrices that can be solved efficiently. Such a presentation provides better sparsity than the celebrated and widely used Daubechies wavelets. These QMF coefficients are dependent on the filter parameters such as transition bandwidth and filter length. It was found that the sharper the transition bandwidth, the greater the reduction in nonzero elements of the impedance matrix. It also can be applied in the wavelet packet algorithm to further sparsify the impedance matrix. Numerical examples are given to demonstrate the effectiveness and validity of our finding.

关键词： impulse response coefficients quadrature mirror filter channel bank filters wavelet packet algorithm Electromagnetic wave propagation impedance matrix Linear algebra (numerical analysis) sparse matrices transition bandwidth filtering methods in signal processing nonzero elements sparse moment matrices Integral transforms in numerical analysis 2D scattering problems sparsity filter parameters transient response wavelet transform matrix electromagnetic wave scattering impedance matrix compression quadrature mirror filters lattice filters QMF coefficients wavelet transforms effective quadrature filter filter length

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Fast spatial combinative lifting algorithm of wavelet transform using the 9/7 filter for image block compression

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ELECTRONICS LETTERS 2000年第21期36卷 1766-1767页

作者： Meng, HY Wang, ZH Tsing Hua Univ Dept Elect Engn Beijing 100084 Peoples R China

A new fast spatial combinative lifting algorithm (SCLA) of the wavelet transform using the 9/7 filter for image block compression is proposed. In comparison with its lifting-based implementation, the number of multipl... 详细信息

关键词： wavelet transforms fast spatial combinative lifting algorithm filtering theory image block compression data compression Image and video coding Computer vision and image processing techniques Integral transforms in numerical analysis filtering methods in signal processing image coding 9/7 filter multiplications reduction matrix algebra Linear algebra (numerical analysis) signal processing theory wavelet transform

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Modified RLS algorithm for unbiased estimation of FIR system with input and output noise

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ELECTRONICS LETTERS 2000年第3期36卷 273-274页

作者： Feng, DZ Bao, Z Zhang, XD Xidian Univ Kay Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In the presence of both input and output noise, the classical least squares solution for finite-impulse response (FIR) estimation is biased. It has been shown that bias can be removed by properly scaling the optimal FIR filter coefficients in the least-squares (LS) criterion. A modified recursive least squares (MRLS) algorithm is proposed for accurate identification of a system with both input and output noise. Simulation results show that this method outperforms the modified LMS algorithm under non-stationary interference conditions.

关键词： least squares approximations nonstationary interference Simulation, modelling and identification filtering methods in signal processing FIR filters modified recursive least squares algorithm adaptive system identification unbiased estimation Digital filters output noise finite impulse response filter signal processing theory input noise Interpolation and function approximation (numerical analysis) recursive estimation

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