Showing papers on "Adaptive filter published in 1998"

A multistage representation of the Wiener filter based on orthogonal projections

Abstract: The Wiener filter is analyzed for stationary complex Gaussian signals from an information theoretic point of view. A dual-port analysis of the Wiener filter leads to a decomposition based on orthogonal projections and results in a new multistage method for implementing the Wiener filter using a nested chain of scalar Wiener filters. This new representation of the Wiener filter provides the capability to perform an information-theoretic analysis of previous, basis-dependent, reduced-rank Wiener filters. This analysis demonstrates that the cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the desired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods.

Blind multiuser detection: a subspace approach

Abstract: A new multiuser detection scheme based on signal subspace estimation is proposed. It is shown that under this scheme, both the decorrelating detector and the linear minimum-mean-square-error (MMSE) detector can be obtained blindly, i.e., they can be estimated from the received signal with the prior knowledge of only the signature waveform and timing of the user of interest. The consistency and asymptotic variance of the estimates of the two linear detectors are examined. A blind adaptive implementation based on a signal subspace tracking algorithm is also developed. It is seen that compared with the previous minimum-output-energy blind adaptive multiuser detector, the proposed subspace-based blind adaptive detector offers lower computational complexity, better performance, and robustness against signature waveform mismatch. Two extensions are made within the framework of signal subspace estimation. First, a blind adaptive method is developed for estimating the effective user signature waveform in the multipath channel. Secondly, a multiuser detection scheme using spatial diversity in the form of an antenna array is considered. A blind adaptive technique for estimating the array response for diversity combining is proposed. It is seen that under the proposed subspace approach, blind adaptive channel estimation and blind adaptive array response estimation can be integrated with blind adaptive multiuser detection, with little attendant increase in complexity.

Application issues of active power filters

Abstract: In this article, common nonlinear loads have been characterized into two types of harmonic sources, current-source type of harmonic source and voltage-source type of harmonic source. Compensation characteristics of both parallel active filters and series active filters have been discussed analytically and experimentally for these two types of harmonic sources. The corresponding required operation conditions, features, application issues, and adaptive harmonic sources of both filters have been presented. The fact that the traditional active filter, the parallel active filter, is not a panacea to harmonic compensation, and that one cannot use it blindly, has been clearly addressed. The parallel active filter will increase harmonic current and may cause overcurrent of the load when the load is a harmonic voltage source. Instead, it has been verified that the series active filter is better suited for compensation of a harmonic voltage source such as a diode rectifier with smoothing DC capacitor. The conclusions of this article also imply that when a parallel active filter is installed in a power system network such as at a point of common coupling, the network impedance and main harmonic sources downstream from the installation point should be investigated in order to get good performance and to minimize influence to the loads downstream. In some cases, a combined system of parallel active filter and series active filter may be necessary by utilizing the harmonic isolation function of the series active filters. No doubt active filters are superior to passive filters if used in their niche applications.

Adaptive blind signal processing-neural network approaches

Abstract: Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.

Learning from data

Abstract: = Abstract An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Tracking and Kalman Filtering Made Easy

Abstract: TRACKING, PREDICTION, AND SMOOTHING BASICS. g and g-h-k Filters. Kalman Filter. Practical Issues for Radar Tracking. LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAY PROCESSING, AND EXTENDED KALMAN FILTER. Least-Squares and Minimum-Variance Estimates for Linear Time-Invariant Systems. Fixed-Memory Polynomial Filter. Expanding- Memory (Growing-Memory) Polynomial Filters. Fading-Memory (Discounted Least-Squares) Filter. General Form for Linear Time-Invariant System. General Recursive Minimum-Variance Growing-Memory Filter (Bayes and Kalman Filters without Target Process Noise). Voltage Least-Squares Algorithms Revisited. Givens Orthonormal Transformation. Householder Orthonormal Transformation. Gram--Schmidt Orthonormal Transformation. More on Voltage-Processing Techniques. Linear Time-Variant System. Nonlinear Observation Scheme and Dynamic Model (Extended Kalman Filter). Bayes Algorithm with Iterative Differential Correction for Nonlinear Systems. Kalman Filter Revisited. Appendix. Problems. Symbols and Acronyms. Solution to Selected Problems. References. Index.

A self-organizing state-space model

Abstract: A self-organizing filter and smoother for the general nonlinear non-Gaussian state-space model is proposed. An expanded state-space model is defined by augmenting the state vector with the unknown parameters of the original state-space model. The state of the augmented state-space model, and hence the state and the parameters of the original state-space model, are estimated simultaneously by either a non-Gaussian filter/smoother or a Monte Carlo filter/smoother. In contrast to maximum likelihood estimation of model parameters in ordinary state-space modeling, for which the recursive filter computation has to be done many times, model parameter estimation in the proposed self-organizing filter/smoother is achieved with only two passes of the recursive filter and smoother operations. Examples such as automatic tuning of dispersion and the shape parameters, adaptation to changes of the amplitude of a signal in seismic data, state estimation for a nonlinear state space model with unknown parameters. and seasonal adjustment with a nonlinear model with changing variance parameters are shown to exemplify the usefulness of the proposed method.

Oversampled filter banks

Abstract: Perfect reconstruction oversampled filter banks are equivalent to a particular class of frames in l/sup 2/(Z). These frames are the subject of this paper. First, the necessary and sufficient conditions of a filter bank for implementing a frame or a tight frame expansion are established, as well as a necessary and sufficient condition for perfect reconstruction using FIR filters after an FIR analysis. Complete parameterizations of oversampled filter banks satisfying these conditions are given. Further, we study the condition under which the frame dual to the frame associated with an FIR filter bank is also FIR and give a parameterization of a class of filter banks satisfying this property. Then, we focus on non-subsampled filter banks. Non-subsampled filter banks implement transforms similar to continuous-time transforms and allow for very flexible design. We investigate the relations of these filter banks to continuous-time filtering and illustrate the design flexibility by giving a procedure for designing maximally flat two-channel filter banks that yield highly regular wavelets with a given number of vanishing moments.

A new technique for noise filtering of SAR interferometric phase images

Abstract: This paper addresses the noise filtering problem for synthetic aperture radar (SAR) interferometric phase images. The phase noise is characterized by an additive noise model. The model is verified with an L-band shuttle imaging radar (SIR)-C interferogram. An adaptive filtering algorithm based on this noise model is developed. It emphasizes filtering noise adaptively according to the local noise level and filtering along fringes using directionally dependent windows. This algorithm is effective, especially for the tightly packed fringes of X-band interferometry. Using simulated and SIR-C/X-SAR repeat-pass generated interferograms, the effectiveness of this filter is demonstrated by its capabilities in residue reduction, adaptive noise filtering, and its ability to filter areas with high fringe rates. In addition, a scheme of incorporating this filtering algorithm in iterative phase unwrapping using a least-squares method is proposed.

Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise

Abstract: The quality of a computed tomography (CT) image is often degraded by streaking artifacts resulting from excessive x-ray quantum noise. Often, a patient has to be rescanned at a higher technique or at a larger slice thickness in order to obtain an acceptable image for diagnosis. This results in a higher dose to the patient, a degraded cross plane resolution, or a reduced patient throughput. In this paper, we propose an adaptive filtering approach in Radon space based on the local statistical properties of the CT projections. We first model the noise characteristics of a projection sample undergoing important preprocessing steps. A filter is then designed such that its parameters are dynamically adjusted to adapt to the local noise characteristics. Because of the adaptive nature of the filter, a proper balance between streak artifact suppression and spatial resolution preservation is achieved. Phantom and clinical studies have been conducted to evaluate the robustness of our approach. Results demonstrate that the adaptive filtering approach is effective in reducing or eliminating quantum noise induced artifacts in CT. At the same time, the impact on the spatial resolution is kept at a low level.

Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size

Abstract: Set-membership identification (SMI) theory is extended to the more general problem of linear-in-parameters filtering by defining a set-membership specification, as opposed to a bounded noise assumption. This sets the framework for several important filtering problems that are not modeled by a "true" unknown system with bounded noise, such as adaptive equalization, to exploit the unique advantages of SMI algorithms. A recursive solution for set membership filtering is derived that resembles a variable step size normalized least mean squares (NLMS) algorithm. Interesting properties of the algorithm, such as asymptotic cessation of updates and monotonically non-increasing parameter error, are established. Simulations show significant performance improvement in varied environments with a greatly reduced number of updates.

Adaptive AR modeling of nonstationary time series by means of Kalman filtering

Abstract: An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: first, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning.

ISAR motion compensation via adaptive joint time-frequency technique

Abstract: A novel approach for inverse synthetic aperture radar (ISAR) imaging is presented for both target translational motion and rotational motion nonuniformity compensation. The basic idea is to perform Doppler tracking to individual scatterers via an adaptive joint time-frequency (AJTF) projection technique. After maximizing the projection of the phase function to a set of basis functions in time-frequency plane, the Doppler frequency drift of the strongest scatterer in the range bin is automatically tracked out and the multiple prominent point processing (PPP) scheme is implemented to eliminate both the translational motion error and rotational motion nonuniformity. Further the azimuth spacing can be estimated, which permits polar reformatting of the original collected data.

Adaptive denoising based on SURE risk

Abstract: A new adaptive denoising method is presented based on Stein's (1981) unbiased risk estimate (SURE) and on a new class of thresholding functions. First, we present a new class of thresholding functions that has a continuous derivative while the derivative of standard soft-thresholding function is not continuous. The new thresholding functions make it possible to construct the adaptive algorithm whenever using the wavelet shrinkage method. By using the new thresholding functions, a new adaptive denoising method is presented based on SURE. Several numerical examples are given. The results indicated that for denoising applications, the proposed method is very effective in adaptively finding the optimal solution in a mean square error (MSE) sense. It is also shown that this method gives better MSE performance than those conventional wavelet shrinkage methods.

Image contrast enhancement based on a histogram transformation of local standard deviation

Abstract: The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CGs) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (LSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, the authors present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's (1976) image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using the authors' formulation, it can be shown that conventional ACEs use linear functions to compute the new CGs. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of the the authors' new algorithm.

A general weighted median filter structure admitting negative weights

Abstract: Weighted median smoothers, which were introduced by Edgemore in the context of least absolute regression over 100 years ago, have received considerable attention in signal processing during the past two decades. Although weighted median smoothers offer advantages over traditional linear finite impulse response (FIR) filters, it is shown in this paper that they lack the flexibility to adequately address a number of signal processing problems. In fact, weighted median smoothers are analogous to normalized FIR linear filters constrained to have only positive weights. It is also shown that much like the mean is generalized to the rich class of linear FIR filters, the median can be generalized to a richer class of filters admitting positive and negative weights. The generalization follows naturally and is surprisingly simple. In order to analyze and design this class of filters, a new threshold decomposition theory admitting real-valued input signals is developed. The new threshold decomposition framework is then used to develop fast adaptive algorithms to optimally design the real-valued filter coefficients. The new weighted median filter formulation leads to significantly more powerful estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior weighted median smoother structures.

A game theoretic fault detection filter

Abstract: The fault detection process is approximated with a disturbance attenuation problem. The solution to this problem, for both linear time-varying and time-invariant systems, leads to a game theoretic filter which bounds the transmission of all exogenous signals except the fault to be detected. In the limit, when the disturbance attenuation bound is brought to zero, a complete transmission block is achieved by embedding the nuisance inputs into an unobservable, invariant subspace. Since this is the same invariant subspace structure seen in some types of detection filters, we can claim that the asymptotic game filter is itself a detection filter. One can also make use of this subspace structure to reduce the order of the limiting game theoretic filter by factoring this invariant subspace out of the state space. The resulting lower dimensional filter will then be sensitive only to the failure to be detected. A pair of examples given at the end of the paper demonstrate the effectiveness of the filter for time-invariant and time-varying problems in both full-order and reduced-order forms.

Adaptive decision feedback equalization: can you skip the training period?

Abstract: This paper presents a novel unsupervised (blind) adaptive decision feedback equalizer (DFE). It can be thought of as the cascade of four devices, whose main components are a purely recursive filter (/spl Rscr/) and a transversal filter (/spl Tscr/). Its major feature is the ability to deal with severe quickly time-varying channels, unlike the conventional adaptive DFE. This result is obtained by allowing the new equalizer to modify, in a reversible way, both its structure and its adaptation according to some measure of performance such as the mean-square error (MSE). In the starting mode, /spl Rscr/ comes first and whitens its own output by means of a prediction principle, while /spl Tscr/ removes the remaining intersymbol interference (ISI) thanks to the Godard (1980) (or Shalvi-Weinstein (1990)) algorithm. In the tracking mode the equalizer becomes the classical DFE controlled by the decision-directed (DD) least-mean-square (LMS) algorithm. With the same computational complexity, the new unsupervised equalizer exhibits the same convergence speed, steady-state MSE, and bit-error rate (BER) as the trained conventional DFE, but it requires no training. It has been implemented on a digital signal processor (DSP) and tested on underwater communications signals-its performances are really convincing.

A linear receiver for direct-sequence spread-spectrum multiple-access systems with antenna arrays and blind adaptation

Abstract: A linear receiver for direct-sequence spread-spectrum multiple-access communication systems under the aperiodic random sequence model is considered. The receiver consists of the conventional matched filter followed by a tapped delay line with the provision of incorporating the use of antenna arrays. It has the ability of suppressing multiple-access interference (MAI) and narrowband interference in some weighted proportions, as well as combining multipath components without explicit estimation of any channel conditions. Under some specific simplified channel models, the receiver reduces to the minimum variance distortionless response beamformer, the RAKE receiver, a notch filter, or an MAI suppressor. The interference rejection capability is made possible through a suitable choice of weights in the tapped delay line. The optimal weights can be obtained by straightforward but computationally complex eigenanalysis. In order to reduce the computational complexity, a simple blind adaptive algorithm is also developed.

Adaptive frequency-domain equalization and diversity combining for broadband wireless communications

Abstract: We introduce a new kind of adaptive equalizer that operates in the spatial-frequency domain, and uses either least mean square (LMS) or recursive least squares (RLS) adaptive processing. We simulate the equalizer's performance in an 8 Mb/s QPSK (quaternary phase shift keying) link over a frequency-selective, Rayleigh fading multipath channel with /spl sim/3 /spl mu/s RMS delay spread, corresponding to 60 symbols of dispersion. Our results show rapid convergence and tracking for a range of mobile speeds (up to /spl sim/100 mph). Moreover, a 2-branch RLS equalizer requires only /spl sim/50 complex operations per detected bit, which, at 8 Mb/s, is close to achievable with state-of-the-art digital signal processing technology.

High-quality audio compression using an adaptive wavelet packet decomposition and psychoacoustic modeling

Abstract: This paper presents a technique to incorporate psychoacoustic models into an adaptive wavelet packet scheme to achieve perceptually transparent compression of high-quality (34.1 kHz) audio signals at about 45 kb/s. The filter bank structure adapts according to psychoacoustic criteria and according to the computational complexity that is available at the decoder. This permits software implementations that can perform according to the computational power available in order to achieve real time coding/decoding. The bit allocation scheme is an adapted zero-tree algorithm that also takes input from the psychoacoustic model. The measure of performance is a quantity called subband perceptual rate, which the filter bank structure adapts to approach the perceptual entropy (PE) as closely as possible. In addition, this method is also amenable to progressive transmission, that is, it can achieve the best quality of reconstruction possible considering the size of the bit stream available at the encoder. The result is a variable-rate compression scheme for high-quality audio that takes into account the allowed computational complexity, the available bit-budget, and the psychoacoustic criteria for transparent coding. This paper thus provides a novel scheme to marry the results in wavelet packets and perceptual coding to construct an algorithm that is well suited to high-quality audio transfer for Internet and storage applications.

Signal adaptive filtering method, signal adaptive filter and computer readable medium for storing program therefor

Abstract: A signal adaptive filtering method for reducing blocking effect and ringing noise, a signal adaptive filter, and a computer readable medium. The signal adaptive filtering method capable of reducing blocking effect and ringing noise of image data when a frame is composed of blocks of a predetermined size includes the steps of: (a) generating blocking information for reducing the blocking effect and ringing information for reducing the ringing noise, from coefficients of predetermined pixels of the upper and left boundary regions of the data block when a frame obtained by deconstructing a bitstream image data for inverse quantization is an intraframe; and (b) adaptively filtering the image data passed through inverse quantization and inverse discrete cosine transform according to the generated blocking information and ringing information. Therefore, the blocking effect and ringing noise can be eliminated from the image restored from the block-based image, thereby enhancing the image restored from compression.

A family of adaptive filter algorithms with decorrelating properties

Abstract: Although the normalized least mean square (NLMS) algorithm is robust, it suffers from low convergence speed if driven by highly correlated input signals. One method presented to overcome this problem is the Ozeki/Umeda (1984) affine projection (AP) algorithm. The algorithm applies update directions that are orthogonal to the last P input vectors and thus allows decorrelation of an AR(P) input process, speeding up the convergence. This article presents a simple approach to show this property, which furthermore leads to the construction of new algorithms that can handle other kinds of correlations such as MA and ARMA processes. A statistical analysis is presented for this family of algorithms. Similar to the AP algorithm, these algorithms also suffer a possible increase in the noise energy caused by their pre-whitening filters.

Theory and Methods

Abstract: A self-organizing filter and smoother for the general nonlinear non-Gaussian state-space model is proposed. An expanded state-space model is defined by augmenting the state vector with the unknown parameters of the original state-space model. The state of the augmented state-space model, and hence the state and the parameters of the original state-space model, are estimated simultaneously by either a non-Gaussian filter/smoother or a Monte Carlo filter/smoother. In contrast to maximum likelihood estimation of model parameters in ordinary state-space modeling, for which the recursive filter computation has to be done many times, model parameter estimation in the proposed self-organizing filter/smoother is achieved with only two passes of the recursive filter and smoother operations. Examples such as automatic tuning of dispersion and the shape parameters, adaptation to changes of the amplitude of a signal in seismic data, state estimation for a nonlinear state space model with unknown parameters, ...

Matching pursuit filters applied to face identification

Abstract: We present a face identification algorithm that automatically processes an unknown image by locating and identifying the face. The heart of the algorithm is the use of pursuit filters. A matching pursuit filter is an adapted wavelet expansion, where the expansion is adapted to both the data and the pattern recognition problem being addressed. For identification, the filters find the features that differentiate among faces, whereas, for detection, the filters encode the similarities among faces. The filters are designed though a simultaneous decomposition of a training set into a two-dimensional (2-D) wavelet expansion. This yields a representation that is explicitly 2-D and encodes information locally. The algorithm uses coarse to fine processing to locate a small set of key facial features, which are restricted to the nose and eye regions of the Face. The result is an algorithm that is robust to variations in facial expression, hair style, and the surrounding environment. Based on the locations of the facial features, the identification module searches the data base for the identity of the unknown face using matching pursuit filters to make the identification. The algorithm was demonstrated on three sets of images. The first set was images from the FERET data base. The second set was infrared and visible images of the same people. This demonstration was done to compare performance on infrared and visible images individually, and on fusing the results from both modalities. The third set was mugshot data from a law enforcement application.

Design and optimization of IIR filter structure using hierarchical genetic algorithms

Abstract: A new genetic algorithm (GA) is proposed for digital filter design. This scheme utilizes a new hierarchical multilayer gene structure for the chromosome formulation. This is a unique structure, which retains the conventional genetic operations, while the genes may take various forms to represent the system characteristics. As a result, both the system structure and the parametric variables can be optimized in a simultaneous manner, without extra computational cost and effort. It has been demonstrated that this technique not only fulfils all types of filter performance requirements, but that the lowest order of the filter can also be found.

Total least mean squares algorithm

Abstract: Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm. The paper gives the statistical analysis for this algorithm, studies the global asymptotic convergence of this algorithm by an equivalent energy function, and evaluates the performances of this algorithm via computer simulations.

Mitigating effects on STAP processing caused by an inclined array

Abstract: This paper presents several techniques for mitigating the effects of an inclined linear array on STAP algorithms. A new technique, termed Doppler Warping is shown to completely mitigate the effects of the array inclination on the STAP algorithm by shifting the Doppler filters to track the Doppler frequency variation of clutter over range. After establishing the effects of array inclination on STAP algorithms, several techniques are presented to reduce the array inclination effects. Doppler Warping is shown to be computationally very inexpensive and to completely mitigate the effects of array inclination. Covariance analysis results are presented.

Feedback cancellation apparatus and methods

Abstract: Feedback cancellation apparatus uses a cascade of two filters (114, 118) along with a short bulk delay (110). The first filter (114) is adapted when the hearing aid is turned on in the ear. This filter adapts quickly using a white noise probe signal (216), and then the filter coefficients are frozen. The first filter models parts of the hearing-aid feedback path that are essentially constant over the course of the day. The second filter (118) adapts while the hearing aid is in use and does not use a separate probe signal. This filter provides a rapid correction to the feedback path model when the hearing aid goes unstable, and more slowly tracks perturbations in the feedback path that occur in daily use. The delay (110) shifts the filter response to make the most effective use of the limited number of filter coefficients.