Application Of Adaptive Filter Research Articles

特集 ディジタル伝送 カラー画像の適応的可変ブロック形状ＫＬ変換符号化

This paper proposes a new transform coding scheme which partitions a color image composed of luminance signals and two color difference signals into variable-shape-blocks. This partition reduces interference such as mosquito noise and blocking effects. The shapes of the blocks for the above three signals accord with one another in order to reduce the amount of computation required for image segmentation and derivation of basis vectors in a KL-transform, as well as the information load related to the block shapes. This scheme attains segmentation and bit allocation for color images by minimizing the power of residual signals which are obtained through the application of a new adaptive smoothing filter, and the final mean square error in the RGB signal space. Simulation results demonstrate that this scheme improves SNR to about 2 dB more than what is attained by a JPEG Baseline System, and reproduces better images with less interference.

Dosimetric Aspects of a Scanning Multiple Beam Equalisation System for Chest Radiography (Amber)

In routine chest examinations, the variation in the transmission of X rays through the patient often exceeds the dynamic range of the receptor (film-screen combination) resulting in over exposed or under exposed areas on the chest X ray film. Recent technical developments, such as a system for multiple beam scanning equalisation radiography (Amber), provide the possibility of overcoming this problem. The amber technique is based on the application of a two-dimensional real-time adaptive spatial filter. This is achieved by modulation of the X ray intensity as a function of the local patient absorption. Dosimetric aspects of the application of Amber have been investigated and are compared with a conventional technique. Dose measurements are performed at the surface and at the interior of a polymethylmethacrylate (PMMA) and an anthropomorphic phantom using thermoluminescence and ionimetric dosimetry. Entrance doses and organ doses have been derived from the measurements.

Dosimetric Aspects of a Scanning Multiple Beam Equalisation System for Chest Radiography (Amber)

In routine chest examinations, the variation in the transmission of X rays through the patient often exceeds the dynamic range of the receptor (film-screen combination) resulting in over exposed or under exposed areas on the chest X ray film. Recent technical developments, such as a system for multiple beam scanning equalisation radiography (Amber), provide the possibility of overcoming this problem. The amber technique is based on the application of a two-dimensional real-time adaptive spatial filter. This is achieved by modulation of the X ray intensity as a function of the local patient absorption. Dosimetric aspects of the application of Amber have been investigated and are compared with a conventional technique. Dose measurements are performed at the surface and at the interior of a polymethylmethacrylate (PMMA) and an anthropomorphic phantom using thermoluminescence and ionimetric dosimetry. Entrance doses and organ doses have been derived from the measurements.

Frequency-domain implementation of Griffiths–Jim adaptive beamformer

It is well known that the drawback of the time-domain least-mean-square (LMS) algorithm proposed by Widrow [Proc. IEEE 63, 719–720 (1975)] in adaptive filter applications is that its convergence speed decreases as the eigenvalue spread of the input autocorrelation matrix increases. However, this problem can be overcome by employing the transform-domain LMS method [Narayan et al., IEEE Trans. ASSP 31, 609–615 (1983)] which first transforms input time-domain signals into another transform-domain signals through DFT or some orthogonal transforms and then uses the self-orthogonalizing algorithm [Gitlin and Magee, IEEE Trans. Commun. 25, 666–672 (1977)] to optimize the variable weights of an adaptive filter. This method has been shown to offer great improvement in convergence rate over the time-domain LMS method. The objective of this paper is to apply the frequency-domain LMS algorithm with the self-orthogonalizing technique, called FLMS, to the Griffiths–Jim adaptive beamformer to accelerate the convergence rate for real-time processing of adaptive array signals. It is shown that the two minimum mean-square errors of the beamformer implemented in the time and frequency domains are identical. Computer simulations show that the adaptive beamformer using the FLMS exhibits faster convergence behavior and better performance of nulling jammers than that using the LMS, especially for the larger eigenvalue spread.

On the implementation of the frequency-domain LMS adaptive filter

Alternative implementations for the frequency-domain LMS adaptive filter are presented. One of the implementations uses an LMS steepest-descent algorithm to compute the discrete Fourier transform (DFT). Therefore, the frequency-domain LMS adaptive filter is implemented as a cascade of two parts, with each one running the LMS adaptive algorithm. This requires fewer computations than the FFT for large filter lengths. Simulations of practical applications of frequency-domain LMS adaptive filters are carried out to determine their suitability, and comparisons are made with simulations using the FFT. For real input data, another frequency-domain LMS adaptive filter is suggested based on the discrete Hartley transform (DHT). >

A new adaptive noise cancellation scheme in the presence of crosstalk (speech signals)

The application of adaptive filters in noise canceling often requires the relative placement of the two transducers at a distance that necessitates a larger order filter in order to obtain an adequate output signal-to-noise ratio. A new adaptive filter structure is introduced that permits a closer placement of the transducers and that allows the cancellation of noise in the presence of crosstalk. Algorithms are developed for the new transversal and lattice filter estimators. Simulations show considerable improvement in mean-square error over that obtained with standard noise canceling algorithms. >

Application of adaptive filters to noninvasive acoustical detection of coronary occlusions before and after angioplasty.

Previous studies have indicated that coronary stenoses produce sounds due to the turbulent blood flow in these vessels [1]-[10]. Measurement of these signals forms the basis of our noninvasive approach to the detection of coronary artery disease. It is during diastole that coronary blood flow is maximum and the sounds associated with turbulent blood flow through partially occluded coronary arteries would be loudest [1]-[10]. Isolated diastolic heart sounds taken from recordings made at the patient's bedside were modeled using the autoregressive (AR) and autoregressive moving average (ARMA) methods [4], [7] after adaptive line enhancement (ALE). Decisions were made in a blind fashion without prior knowledge of whether a given recording was made before or after angioplasty. Resulting model frequency spectra showed greater high-frequency components (between 400 and 800 Hz) in preangioplasty patients, and a consistent shift in amplitude of the second pole pairs of the AR and ARMA methods with surgery. Blind assessment, based on frequency spectra and poles, correctly classified the diastolic recordings in 18 of 20 cases. These results provide strong evidence supporting our hypothesis that coronary stenoses produce detectable sounds during diastole [1]-[10].

Adaptive polynomial filters

Adaptive nonlinear filters equipped with polynomial models of nonlinearity are explained. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in adaptive filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. Adaptive algorithms using system models involving recursive nonlinear difference equations are then treated. Such systems may be able to approximate many nonlinear systems with great parsimony in the use of coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Gradient-based adaptive algorithms for systems with external feedback paths

The effect on error surface gradient of feedback from output to input of an adaptive filter in a system identification configuration is explored. This feedback can occur in any application of adaptive filters in which the output of the filter is radiated in some way so that it may leak back into the filter input. One example where this can happen is in the active control of acoustic noise, where it is often not possible to acoustically isolate the secondary source (fed by the output of the adaptive filter) from the sound field detector (which provides the input signal for the adaptive filter). It is shown that, since the error surface is altered by the presence of the feedback, the common gradient algorithms are no longer generally appropriate. The circumstances when they will or will not work are delineated.

Adaptive line enhancement by using an IIR lattice notch filter

Two simple methods for retrieving a single sinusoid corrupted with noise are proposed. They are based on the lattice form realization of an adaptive infinite-impulse-response (IIR) notch filter. The IIR filter is a cascade of second-order all-pole and all-zero filters, and the coefficients of the finite-impulse-response (FIR) section are adapted. The proposed algorithms keep the poles of the filter inside the unit circle. The computer simulation results show that the algorithms have considerable potential in adaptive notch filter applications, especially when the input signal-to-noise ratio is low.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

TMS32020 implementation of an adaptive recursive echo canceller

Recently, the application of adaptive recursive digital filters (ARDF) in echo cancellation has received considerable attention. This is because the ARDF can match a long echo path with fewer coefficients than the adaptive transversal digital filter (ATDF). Consequently, the ARDF has a shorter convergence time and smaller adaptation noise than the ATDF. Unfortunately the ARDF may exhibit instability during the adaption process. This paper outlines the development of an adaptive recursive echo canceller which is unconditionally stable. The new filter algorithm has been extensively tested in off-line simulations and this paper describes the on-line implementation of the echo canceller using a TMS32020 system. The signal processor is hosted by an IBM PC and is interfaced to a model data transmission network incorporating actual hybrid transformers. A realistic echo is thus available to evaluate the filter performance.

Theory and application of adaptive fading memory Kalman filters

To resolve difficulties encountered in estimating rigid body motion from noisy radar measurements, the behavior of an adaptive fading-memory Kalman filter is studied. How adaptive fading-memory Kalman filters can be constructed to estimate rigid body motion from radar measurements is described. A review of fading-memory Kalman filters is contained, with emphasis on their interpretation in the frequency domain. The stability analysis of an adaptive fading-memory Kalman filter is performed. Sample behavior of the error state vector is also examined. The application of the adaptive fading-memory Kalman filters to rigid-body motion estimation is briefly described. >

Recirculation of input data in frequency-domain adaptive filtering

The recirculation of input data is proposed in the application of frequency-domain adaptive filtering when the supply of input data is limited. An analysis of the performance of such an algorithm is presented and it indicates that superior performance can be achieved. Examples of its applications in adaptive filtering confirm the results of analysis and demonstrate the effectiveness of the proposed algorithm.

The selection and application of an IIR adaptive filter for use in active sound attenuation

The use of infinite impulse response (IIR) adaptive filters has lagged behind that of finite impulse response (FIR) adaptive filters. This has been due, in part, to the increased complexity of IIR filters and the potential for instability that exists due to the presence of poles in the transfer function. This paper discusses the use of adaptive filters for the active cancellation of acoustic noise. It is shown that IIR filters possess certain characteristics that are highly desirable for this problem. The selection of an appropriate IIR adaptive algorithm is discussed using observability considerations. It is shown that the recursive least mean square (RLMS) algorithm of Feintuch possesses significant advantages for use in a practical active attentuation system. Results are presented from computer simulations as well as an actual system using a TI TMS32010 digital signal processing microprocessor.

The application of the frequency domain LMS adaptive filter to split array bearing estimation with a sinusoidal signal

This paper considers the use of the frequency domain LMS adaptive filter in a split array sonar to estimate the bearing of a plane wave acoustic source radiating a sinusoidal signal. For such a signal, the bearing can be determined from the relative signal phase between split array outputs at the signal frequency. In this paper, the split array outputs are fast Fourier transformed and used as the primary and reference inputs to the frequency domain LMS adaptive filter configured as a canceller. The signal frequency is estimated based upon the magnitude of the weights, and the relative signal phase between split array outputs is estimated as the phase of the frequency domain weight with the largest magnitude. The statistics of the magnitude and phase of the frequency domain weights are determined and then mapped to the variance of the bearing estimate. Computer simulations of the adaptive tracker which verify the analytical results are included.

Design and application of adaptive filters

The rapid spread of digital filters to applications as wide ranging as control-systems modelling, telephony and noise cancellation has led to the evolution of the programmable or adaptive filter. Here we look at the design of the basic adaptive filter and its range of uses

Adaptive equalization

Bandwidth-efficient data transmission over telephone and radio channels is made possible by the use of adaptive equalization to compensate for the time dispersion introduced by the channel Spurred by practical applications, a steady research effort over the last two decades has produced a rich body of literature in adaptive equalization and the related more general fields of reception of digital signals, adaptive filtering, and system identification. This tutorial paper gives an overview of the current state of the art in adaptive equalization. In the first part of the paper, the problem of intersymbol interference (ISI) and the basic concept of transversal equalizers are introduced followed by a simplified description of some practical adaptive equalizer structures and their properties. Related applications of adaptive filters and implementation approaches are discussed. Linear and nonlinear receiver structures, their steady-state performance and sensitivity to timing phase are presented in some depth in the next part. It is shown that a fractionally spaced equalizer can serve as the optimum receive filter for any receiver. Decision-feedback equalization, decision-aided ISI cancellation, and adaptive filtering for maximum-likelihood sequence estimation are presented in a common framework. The next two parts of the paper are devoted to a discussion of the convergence and steady-state properties of least mean-square (LMS) adaptation algorithms, including digital precision considerations, and three classes of rapidly converging adaptive equalization algorithms: namely, orthogonalized LMS, periodic or cyclic, and recursive least squares algorithms. An attempt is made throughout the paper to describe important principles and results in a heuristic manner, without formal proofs, using simple mathematical notation where possible.

Application of gradient adaptive lattice filters to channel equalisation

In this paper, the potential application of adaptive lattice equalisers based on gradient search adaptive algorithms, is examined. The assessment is based on simulations of equaliser performance in data communications systems which provides a comparison of the relative performance of these adaptive lattice equalisers with transversal equalisers. It highlights the critical balance between rapid convergence and degradation due to algorithm noise, which is involved when selecting the algorithm stepsize. Two new adaptive equaliser approaches are suggested, one based on a timed lattice structure feeding a linear combiner. The other is a short timed lattice structure in cascade with a transversal equaliser. Both approaches are shown to offer fast converging adaptive equalisers for data communications applications, which have a converged error performance superior to that of the direct application of a gradient adaptive lattice equaliser.

Recursive Fixed-Order Covariance Least-Squares Algorithms

This paper derives fixed-order recursive Least-Squares (LS) algorithms that can be used in system identification and adaptive filtering applications such as spectral estimation, and speech analysis and synthesis. These algorithms solve the sliding-window and growing-memory covariance LS estimation problems, and require less computation than both unnormalized and normalized versions of the computationally efficient order-recursive (lattice) covariance algorithms previously presented. The geometric or Hilbert space approach, originally introduced by Lee and Morf to solve the prewindowed LS problem, is used to systematically generate least-squares recursions. We show that combining subsets of these recursions results in prewindowed LS lattice and fixed-order (transversal) algorithms, and in sliding-window and growing-memory covariance lattice and transversal algorithms. The paper discusses both least-squares prediction and joint-process estimation.

Adaptive equalization

Bandwidth-efficient data transmission over telephone and radio channels is made possible by the use of adaptive equalization to compensate for the time dispersion introduced by the channel Spurred by practical applications, a steady research effort over the last two decades has produced a rich body of literature in adaptive equalization and the related more general fields of reception of digital signals, adaptive filtering, and system identification. This tutorial paper gives an overview of the current state of the art in adaptive equalization. In the first part of the paper, the problem of intersymbol interference (ISI) and the basic concept of transversal equalizers are introduced followed by a simplified description of some practical adaptive equalizer structures and their properties. Related applications of adaptive filters and implementation approaches are discussed. Linear and nonlinear receiver structures, their steady-state performance and sensitivity to timing phase are presented in some depth in the next part. It is shown that a fractionally spaced equalizer can serve as the optimum receive filter for any receiver. Decision-feedback equalization, decision-aided ISI cancellation, and adaptive filtering for maximum-likelihood sequence estimation are presented in a common framework. The next two parts of the paper are devoted to a discussion of the convergence and steady-state properties of least mean-square (LMS) adaptation algorithms, including digital precision considerations, and three classes of rapidly converging adaptive equalization algorithms: namely, orthogonalized LMS, periodic or cyclic, and recursive least squares algorithms. An attempt is made throughout the paper to describe important principles and results in a heuristic manner, without formal proofs, using simple mathematical notation where possible.