Showing papers on "Adaptive filter published in 2003"

Fundamentals of adaptive filtering

Abstract: Preface. Acknowledgments. Notation. Symbols. Optimal Estimation. Linear Estimation. Constrained Linear Estimation. Steepest-Descent Algorithms. Stochastic-Gradient Algorithms. Steady-State Performance of Adaptive Filters. Tracking Performance of Adaptive Filters. Finite Precision Effects. Transient Performance of Adaptive Filters. Block Adaptive Filters. The Least-Squares Criterion. Recursive Least-Squares. RLS Array Algorithms. Fast Fixed-Order Filters. Lattice Filters. Laguerre Adaptive Filters. Robust Adaptive Filters. Bibliography. Author Index. Subject Index. AC

Adaptive deblocking filter

Abstract: This paper describes the adaptive deblocking filter used in the H.264/MPEG-4 AVC video coding standard. The filter performs simple operations to detect and analyze artifacts on coded block boundaries and attenuates those by applying a selected filter.

Least-mean-square adaptive filters

Abstract: Contributors.Introduction (Simon Haykin).1. On the Efficiency of Adaptive Algorithms (Berrnard Widrow and Max Kamenetsky).2. Travelling-Wave Model of Long LMS Filters (Hans Butterweck).3. Energy Conservation and the Learning Ability of LMS Adaptive Filters (Ali Sayed & Vitor H. Nascimento).4. On the Robustness of LMS Filters (Babak Hassibi).5. Dimension Analysis for Least-Mean-Square Algorithms (Iven M.Y. Mareels, et al.).6. Control of LMS-Type Adaptive Filters (Eberhard Haensler and Gerhard Uwe Schmidt).7. Affine Projection Algorithms (Steve Gay).8. Proportionate Adaptation: New Paradigms in Adaptive Filters (Zhe Chen, et al.).9. Steady-State Dynamic Weight Behavior in (N)LMS Adaptive Filters (A.A. (Louis) Beex and James R. Zeidler).10. Error Whitening Wiener Filters: Theory and Algorithms (Jose Principe, et al.).Index.

Output filter design for a grid-interconnected three-phase inverter

Abstract: Traditionally, LC filter is used for an inverter power supply. A grid-interconnected inverter, however, has some unique requirements that an LC filter may not be sufficient. This paper comprehensively discusses the design considerations of the output filter for the grid-interconnected inverter. Different passive damping filter solutions are compared and the optimized design guidelines are also proposed. Simulation results are provided to validate the design.

An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation

Abstract: An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

The trilateral filter for high contrast images and meshes

Abstract: We present a new, single-pass nonlinear filter for edge-preserving smoothing and visual detail removal for N dimensional signals in computer graphics, image processing and computer vision applications. Built from two modified forms of Tomasi and Manduchi's bilateral filter, the new "trilateral" filter smoothes signals towards a sharply-bounded, piecewise-linear approximation. Unlike bilateral filters or anisotropic diffusion methods that smooth towards piecewise constant solutions, the trilateral filter provides stronger noise reduction and better outlier rejection in high-gradient regions, and it mimics the edge-limited smoothing behavior of shock-forming PDEs by region nding with a fast min-max stack. Yet the trilateral filter requires only one user-set parameter, filters an input signal in a single pass, and does not use an iterative solver as required by most PDE methods. Like the bilateral filter, the trilateral filter easily extends to N-dimensional signals, yet it also offers better performance for many visual applications including appearance-preserving contrast reduction problems for digital photography and denoising polygonal meshes.

Adaptive Kalman Filtering for Low-cost INS/GPS

Abstract: GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.

Adaptive Kalman Filtering for Vehicle Navigation

Abstract: Kalman filters have been widely used for navigation and system integration. One of the key problems associated with Kalman filters is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS navigation, the manoeuvre of the vehicle and the level of measurement noise are environmental dependent, and hardly to be predicted. Therefore to assign constant noise levels for such applications is not realistic. In this paper, real-time adaptive algorithms are applied to GPS data processing. Two different adaptive algorithms are discussed in the paper. A number of tests have been carried out to compare the performance of the adaptive algorithms with a conventional Kalman filter for vehicle navigation. The test results demonstrate that the new adaptive algorithms are much robust to the sudden changes of vehicle motion and measurement errors.

A class of frequency-domain adaptive approaches to blind multichannel identification

Abstract: We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.

Improving passive filter compensation performance with active techniques

Abstract: This paper presents the performance analysis of a hybrid filter composed of passive and active filters connected in series. The analysis is done by evaluating the influence of passive filter parameters variations and the effects that different active power filter's gain have in the compensation performance of the hybrid scheme. The compensation performance is quantified by evaluating the attenuation factor in a power distribution system energizing high-power nonlinear loads compensated with passive filters and then improved with the connection of a series active power filter. Finally, compensation characteristics of the hybrid topology are tested on a 10-kVA experimental setup.

Space-time turbo equalization in frequency-selective MIMO channels

Abstract: A computationally efficient space-time turbo equalization algorithm is derived for frequency-selective multiple-input-multiple-output (MIMO) channels. The algorithm is an extension of the iterative equalization algorithm by Reynolds and Wang (see Signal Processing, vol.81, no.5, p.989-995, 2001) for frequency-selective fading channels and of iterative multiuser detection for code-division multiple-access (CDMA) systems by Wang and Poor (see IEEE Trans. Commun., vol.47, p.1046-1061, 1999). The proposed algorithm is implemented as a MIMO detector consisting of a soft-input-soft-output (SISO) linear MMSE detector followed by SISO channel decoders for the multiple users. The detector first forms a soft replica of each composite interfering signal using the log likelihood ratio (LLR), fed back from the SISO channel decoders, of the transmitted coded symbols and subtracts it from the received signal vector. Linear adaptive filtering then takes place to suppress the interference residuals: filter taps are adjusted based on the minimum mean square error (MMSE) criterion. The LLR is then calculated for adaptive filter output. This process is repeated in an iterative fashion to enhance signal-detection performance. This paper also discusses the performance sensitivity of the proposed algorithm to channel-estimation error. A channel-estimation scheme is introduced that works with the iterative MIMO equalization process to reduce estimation errors.

Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

Abstract: In this paper, we see adaptive control as a three-part adaptive-filtering problem. First, the dynamical system we wish to control is modeled using adaptive system-identification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimum-phase or nonminimum-phase, linear or nonlinear, single-input-single-output (SISO) or multiple-input-multiple-ouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well.

Loop-filtering method for image data and apparatus therefor

Abstract: A loop-filtering method for reducing quantization effect generated when an image data is encoded and decoded, and an apparatus therefor. The loop-filtering method includes the steps of extracting a flag indicating whether the image data requires loop-filtering using the distribution of inverse quantized coefficients (IQCs) of an inverse quantized image data and a motion vector indicating the difference between the previous frame and the current frame. The image data corresponding to the flag is then filtered by a predetermined method if the extracted flag indicates a need for the loop-filtering. Using the flags and an adaptive filter reduces the quantization effect and is useful to reduce the amount of computation required for the filtering. Also, the filtering can be performed through parallel processing without multiplication and division, so that the complexity of hardware can be reduced.

Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration

Abstract: We propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for applications with multiframe environments. Since the registration error in each low-resolution image has a different pattern, the regularization parameters are determined adaptively for each channel. We propose two methods for estimating the regularization parameter automatically. The proposed algorithms are robust against registration error noise, and they do not require any prior information about the original image or the registration error process. Information needed to determine the regularization parameter and to reconstruct the image is updated at each iteration step based on the available partially reconstructed image. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.

Adaptive Signal Processing: Applications to Real-World Problems

Abstract: 1 On a Class of Exponentiated Adaptive Algorithms for the Identification of Sparse Impulse Responses.- 2 Adaptive Feedback Cancellation in Hearing Aids.- 3 Single-Channel Acoustic Echo Cancellation.- 4 Multichannel Frequency-Domain Adaptive Filtering with Application to Multichannel Acoustic Echo Cancellation.- 5 Filtering Techniques for Noise Reduction and Speech Enhancement.- 6 Adaptive Beamforming for Audio Signal Acquisition.- 7 Blind Source Separation of Convolutive Mixtures of Speech.- 8 Adaptive Multichannel Time Delay Estimation Based on Blind System Identification for Acoustic Source Localization.- 9 Algorithms for Adaptive Equalization in Wireless Applications.- 10 Adaptive Space-Time Processing for Wireless CDMA.- 11 The IEEE 802.11 System with Multiple Receive Antennas.- 12 Adaptive Estimation of Clock Skew and Different Types of Delay in the Internet Network.

Transient analysis of data-normalized adaptive filters

Abstract: This paper develops an approach to the transient analysis of adaptive filters with data normalization. Among other results, the derivation characterizes the transient behavior of such filters in terms of a linear time-invariant state-space model. The stability, of the model then translates into the mean-square stability of the adaptive filters. Likewise, the steady-state operation of the model provides information about the mean-square deviation and mean-square error performance of the filters. In addition to deriving earlier results in a unified manner, the approach leads to stability and performance results without restricting the regression data to being Gaussian or white. The framework is based on energy-conservation arguments and does not require an explicit recursion for the covariance matrix of the weight-error vector.

Digital Signal and Image Processing

Abstract: Chapter 1. Fundamental Concepts.Chapter 2. Fourier Analysis.Chapter 3. Z-Transform and Digital Filters.Chapter 4. Filter Design and Implementation.Chapter 5. Multivariate Signal Processing.Chapter 6. Finite-Wordlength Effects.Chapter 7. Adaptive Signal Processing.Chapter 8. Least-Squares Adaptive Algorithms.Chapter 9. Linear Prediction.Chapter 10. Image Processing Fundamentals.Chapter 11. Image Compression and Coding.Appendix. Concepts of Linear Algebra.Index.

Nonlinear acoustic echo cancellation based on Volterra filters

Abstract: This paper describes a nonlinear acoustic echo cancellation algorithm, mainly focused on loudspeaker distortions. The proposed system is composed of two distinct modules organized in a cascaded structure: a nonlinear module based on polynomial Volterra filters models the loudspeaker, and a second module of standard linear filtering identifies the impulse response of the acoustic path. The tracking of the overall system model is achieved by a modified normalized-least mean square algorithm for which equations are derived. Stability conditions are given, and particular attention is placed on the transient behavior of cascaded filters. Finally, results of real data recorded with Alcatel GSM material are presented.

Ultra-low-power DLMS adaptive filter for hearing aid applications

Abstract: We present an ultra-low-power, delayed least mean square (DLMS) adaptive filter operating in the subthreshold region for hearing aid applications. Subthreshold operation was accomplished by using a parallel architecture with pseudo nMOS logic style. The parallel architecture enabled us to operate the system at a lower clock rate and reduced supply voltage while maintaining the same throughput. Pseudo nMOS logic operating in the subthreshold region (subpseudo nMOS) provided better power-delay product than subthreshold CMOS (sub-CMOS) logic. Simulation results show that the DLMS adaptive filter can operate at 22 kHz using a 400-mV supply voltage to achieve 91% improvement in power compared to a nonparallel, CMOS implementation. To validate the robust operation of subthreshold logics, a 0.35 /spl mu/m, 23.1 kHz, 21.4 nW, 8/spl times/8 carry save array multiplier test chip was fabricated where an adaptive body biasing scheme is used for compensating process, supply and temperature variations. The test chip showed stable operation at a supply voltage of 0.30 V, which is even lower than the threshold voltages of the pMOS (0.82 V) and nMOS (0.67 V) transistors.

Adaptive Wiener filtering of noisy images and image sequences

Abstract: In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.

Method and system for processing subband signals using adaptive filters

Abstract: A method and system for processing subband signals using adaptive filters is provided. The system is implemented on an oversampled WOLA filterbank. Inputs signals are oversampled. The system includes an adaptive filter for each subband, and the functionality of improving the convergence properties of the adaptive filter. For example, the convergence property is improved by whitening the spectra of the oversampled subband signals and/or affine projection algorithm. The system is applicable to echo and/or noise cancellation. Adaptive step size control, adaptation process control using Double-Talk detector may be implemented. The system may further implement a non-adaptive processing for reducing uncorrelated noise and/or cross-talk resistant adaptive noise cancellation.

Set-membership binormalized data-reusing LMS algorithms

Abstract: This paper presents and analyzes novel data selective normalized adaptive filtering algorithms with two data reuses. The algorithms [the set-membership binormalized LMS (SM-BN-DRLMS) algorithms] are derived using the concept of set-membership filtering (SMF). These algorithms can be regarded as generalizations of the previously proposed set-membership NLMS (SM-NLMS) algorithm. They include two constraint sets in order to construct a space of feasible solutions for the coefficient updates. The algorithms include data-dependent step sizes that provide fast convergence and low-excess mean-squared error (MSE). Convergence analyzes in the mean squared sense are presented, and closed-form expressions are given for both white and colored input signals. Simulation results show good performance of the algorithms in terms of convergence speed, final misadjustment, and reduced computational complexity.

Transient analysis of adaptive filters with error nonlinearities

Abstract: The paper develops a unified approach to the transient analysis of adaptive filters with error nonlinearities. In addition to deriving earlier results in a unified manner, the approach also leads to new performance results without restricting the regression data to being Gaussian or white. The framework is based on energy-conservation arguments and avoids the need for explicit recursions for the covariance matrix of the weight-error vector.

A robust online secondary path modeling method with auxiliary noise power scheduling strategy and norm constraint manipulation

Abstract: In many practical cases for active noise control (ANC), the online secondary path modeling methods that use auxiliary noise are often applied. However, the auxiliary noise contributes to residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, a sudden and large change in the secondary path leads to easy divergence of the existing online secondary path modeling methods. To mitigate these problems, this paper proposes a new online secondary path modeling method with auxiliary noise power scheduling and adaptive filter norm manipulation. The auxiliary noise power is scheduled based on the convergence status of an ANC system with consideration of the variation of the primary noise. The purpose is to alleviate the increment of the residual noise due to the auxiliary noise. In addition, the norm manipulation is applied to adaptive filters in the ANC system. The objective is to avoid over-updates of adaptive filters due to the sudden large change in the secondary path and thus prevent the ANC system from diverging. Computer simulations show the effectiveness and robustness of the proposed method.

Data record-based criteria for the selection of an auxiliary vector estimator of the MMSE/MVDR filter

Abstract: When the auxiliary vector (AV) filter generation algorithm utilizes sample average estimated input data statistics, it provides a sequence of estimates of the ideal minimum mean-square error or minimum-variance distortionless-response filter for the given signal processing/receiver design application. Evidently, early nonasymptotic elements of the sequence offer favorable bias/variance balance characteristics and outperform in mean-square filter estimation error the unbiased sample matrix inversion (SMI) estimator as well as the (constraint) least-mean square, recursive least-squares, "multistage nested Wiener filter", and diagonally-loaded SMI filter estimators. Selecting the most successful (in some appropriate sense) AV filter estimator in the sequence for a given data record is a critical problem that has not been addressed so far. We deal exactly with this problem and we propose two data-driven selection criteria. The first criterion minimizes the cross-validated sample average variance of the AV filter output and can be applied to general filter estimation problems; the second criterion maximizes the estimated J-divergence of the AV filter output conditional distributions and is tailored to binary phase-shift-keying-type detection problems.

An Adaptive Kalman Filter Based on Sage Windowing Weights and Variance Components

Abstract: In this paper a brief review of Sage adaptive filtering is followed by an analysis of the shortcomings of covariance matrices formed by windowing residual vectors, innovation vectors and correction vectors of the dynamic states. A new adaptive Kalman filter is developed by combining the Sage filter and the variance components and its use tested against various other schemes.

An adaptive filter for synchronous extraction of harmonics and distortions

Abstract: This paper provides an adaptive filter for synchronous detection and extraction of harmonics. The filter can be used as integral part of the control system of a power electronic apparatus (e.g., STATCOM, APF, and UPFC) to generate the desired control signals. Stability and convergence analyses of the adaptive filter are presented based on the dynamical systems theory. Performance of the filter is verified as a means for reference signal generation in a shunt active power filter.