Showing papers on "Adaptive filter published in 1999"

Active noise control: a tutorial review

Abstract: Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.

Adaptive Filters Theory and Applications

Abstract: From the Publisher: Adaptive filtering is an advanced and growing field in signal processing. A filter is a transmission network used in electronic circuits for the selective enhancement or reduction of specified components of an input signal. Filtering is achieved by selectively attenuating those components of the input signal which are undesired, relative to those which it is desired to enhance. This comprehensive book is both a valuable student resource and a useful technical reference for signal processing engineers in industry. The author is experienced in teaching graduates and practicing engineers and the text offers good theoretical coverage complemented by plenty of application examples.

Adaptive Kalman Filtering for INS/GPS

Abstract: After reviewing the two main approaches of adaptive Kalman filtering, namely, innovation-based adaptive estimation (IAE) and multiple-model-based adaptive estimation (MMAE), the detailed development of an innovation-based adaptive Kalman filter for an integrated inertial navigation system/global positioning system (INS/GPS) is given. The developed adaptive Kalman filter is based on the maximum likelihood criterion for the proper choice of the filter weight and hence the filter gain factors. Results from two kinematic field tests in which the INS/GPS was compared to highly precise reference data are presented. Results show that the adaptive Kalman filter outperforms the conventional Kalman filter by tuning either the system noise variance–covariance (V–C) matrix `Q' or the update measurement noise V–C matrix `R' or both of them.

Improved particle filter for nonlinear problems

Abstract: The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required. Methods known generically as 'particle filters' are considered. These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available. In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point. A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation. This is illustrated with the classic bearings-only tracking problem.

Adaptive fuzzy segmentation of magnetic resonance images

Abstract: An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, the authors fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, they also describe a new faster multigrid-based algorithm for its implementation. They show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

Tri-state median filter for image denoising

Abstract: A novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.

Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing

Abstract: Fundamentals of discrete-time signal processing random variables, vectors, and sequences linear signal models nonparametric power spectrum estimation optimum linear filters algorithms and structure for optimum linear filters least-squares filtering and prediction signal modelling and parametric spectral estimation adaptive filters array processing further topics. Appendices: useful results from matrix algebra and optimization theory MATLAB functions.

A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters

Abstract: This paper proposes a new robust adaptive beamformer applicable to microphone arrays. The proposed beamformer is a generalized sidelobe canceller (GSC) with a new adaptive blocking matrix using coefficient-constrained adaptive filters (CCAFs) and a multiple-input canceller with norm-constrained adaptive filters (NCAFs). The CCAFs minimize leakage of the target-signal into the interference path of the GSC. Each coefficient of the CCAFs is constrained to avoid mistracking. The input signal to all the CCAFs is the output of a fixed beamformer. In the multiple-input canceller, the NCAFs prevent undesirable target-signal cancellation when the target-signal minimization at the blocking matrix is incomplete. The proposed beamformer is shown to be robust to target-direction errors as large as 200 with almost no degradation in interference-reduction performance, and it can be implemented with several microphones. The maximum allowable target-direction error can be specified by the user. Simulated anechoic experiments demonstrate that the proposed beamformer cancels interference by over 30 dB. Simulation with real acoustic data captured in a room with 0.3-s reverberation time shows that the noise is suppressed by 19 dB. In subjective evaluation, the proposed beamformer obtains 3.8 on a five-point mean opinion score scale, which is 1.0 point higher than the conventional robust beamformer.

Acoustic echo control. An application of very-high-order adaptive filters

Abstract: We have discussed the application of high-order adaptive filters to the problem of acoustical echo cancellation with particular application to hands free telephone systems. We described a means to achieve robust performance. We further presented methods for reducing computational complexity that allow implementation in low-cost, fixed-point digital signal processors. Progress in technology will allow the use of more sophisticated algorithms at lower cost in the near future.

A globally convergent frequency estimator

Abstract: Online estimation of the frequency of a sinusoidal signal is a classical problem in systems theory that has many practical applications. In this paper the authors provide a solution to the problem of ensuring a globally convergent estimation. More specifically, they propose a new adaptive notch filter whose dynamic equations exhibit the following remarkable features: 1) all signals are globally bounded and the estimated frequency is asymptotically correct for all initial conditions and all frequency values; 2) the authors obtain a simple tuning procedure for the estimator design parameters, which trades-off the adaptation tracking capabilities with noise sensitivity, ensuring (exponential) stability of the desired orbit; and 3) transient performance is considerably enhanced, even for small or large frequencies, as witnessed by extensive simulations. To reveal some of the stability-instability mechanisms of the existing algorithms and motivate our modifications the authors make appeal to a novel nonlinear (state-dependent) time scaling. The main advantage of working in the new time scale is that they remove the coupling between the parameter update law and the filter itself, decomposing the system into a feedback form where the required modifications to ensure stability become apparent. Even though they limit their attention here to the simplest case of a single constant frequency without noise the algorithm is able to track time-varying frequencies, preserve local stability in the presence of multiple sinusoids, and is robust with respect to noise.

An improved particle filter for non-linear problems

Abstract: The Kalman filter provides an effective solution to the linear Gaussian filtering problem However where there is nonlinearity, either in the model specification or the observation process, other methods are required Methods known generically as `particle filters' are considered These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation This is illustrated with the classic bearings-only tracking problem

Superresolution restoration of an image sequence: adaptive filtering approach

Abstract: This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.

Super-resolution reconstruction of image sequences

Abstract: In an earlier work (1999), we introduced the problem of reconstructing a super-resolution image sequence from a given low resolution sequence. We proposed two iterative algorithms, the R-SD and the R-LMS, to generate the desired image sequence. These algorithms assume the knowledge of the blur, the down-sampling, the sequences motion, and the measurements noise characteristics, and apply a sequential reconstruction process. It has been shown that the computational complexity of these two algorithms makes both of them practically applicable. In this paper, we rederive these algorithms as approximations of the Kalman filter and then carry out a thorough analysis of their performance. For each algorithm, we calculate a bound on its deviation from the Kalman filter performance. We also show that the propagated information matrix within the R-SD algorithm remains sparse in time, thus ensuring the applicability of this algorithm. To support these analytical results we present some computer simulations on synthetic sequences, which also show the computational feasibility of these algorithms.

Efficient least squares adaptive algorithms for FIR transversal filtering

Abstract: A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations.

Adaptive SVM to compensate DC-link voltage ripple for four-switch three-phase voltage-source inverters

Abstract: An adaptive space vector modulation (SVM) approach to compensate the DC-link voltage ripple in a B4 inverter is proposed and examined in detail. The theory, design, and performance of this pulsewidth modulation (PWM) method are presented, and the method effectiveness is demonstrated by extensive simulations and experiments. High-quality output currents are guaranteed by this approach even with substantial DC-voltage variations that might be caused by an unbalanced AC supply system, the diode rectification of the line voltages, and circulation of one output phase current through the split capacitor bank. The application of this approach to induction machine drives is also discussed. It is concluded that the DC-voltage ripple effect on the B4 inverter output can be minimized by an adaptive SVM algorithm with the advantage of improving the response of the DC-link filter and the output quality of the inverter becoming high.

A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing

Abstract: This paper presents a novel speckle suppression method for medical B-scan ultrasonic images. An original image is first separated into two parts with an adaptive filter. These two parts are then transformed into a multiscale wavelet domain and the wavelet coefficients are processed by a soft thresholding method, which is a variation of Donoho's (1995) soft thresholding method. The processed coefficients for each part are then transformed back into the space domain. Finally, the denoised image is obtained as the sum of the two processed parts. A computer-simulated image and an in vitro B-scan image of a pig heart have been used to test the performance of this new method. This technique effectively reduces the speckle noise, while preserving the resolvable details. It performs well in comparison to the multiscale thresholding technique without adaptive preprocessing and two other speckle-suppression methods.

On denoising and best signal representation

Abstract: We propose a best basis algorithm for signal enhancement in white Gaussian noise. The best basis search is performed in families of orthonormal bases constructed with wavelet packets or local cosine bases. We base our search for the "best" basis on a criterion of minimal reconstruction error of the underlying signal. This approach is intuitively appealing, because the enhanced or estimated signal has an associated measure of performance, namely, the resulting mean-square error. Previous approaches in this framework have focused on obtaining the most "compact" signal representations, which consequently contribute to effective denoising. These approaches, however, do not possess the inherent measure of performance which our algorithm provides. We first propose an estimator of the mean-square error, based on a heuristic argument and subsequently compare the reconstruction performance based upon it to that based on the Stein (1981) unbiased risk estimator. We compare the two proposed estimators by providing both qualitative and quantitative analyses of the bias term. Having two estimators of the mean-square error, we incorporate these cost functions into the search for the "best" basis, and subsequently provide a substantiating example to demonstrate their performance.

A deblocking filter with two separate modes in block-based video coding

Abstract: This paper presents a method to remove blocking artifacts in low bit-rate block-based video coding. The proposed algorithm has two separate filtering modes, which are selected by pixel behavior around the block boundary. In each mode, proper one-dimensional filtering operations are performed across the block boundary along the horizontal and vertical directions, respectively. In the first mode, corresponding to flat regions, a strong filter is applied inside the block as well as on the block boundary because the flat regions are more sensitive to the human visual system (HVS) and the artifacts propagated from the previous frame due to motion compensation are distributed inside the block. In the second mode, corresponding to other regions, a sophisticated smoothing filter which is based on the frequency information around block boundaries, is used to reduce blocking artifacts adaptively without introducing undesired blur. Even though the proposed deblocking filter is quite simple, it improves both subjective and objective image quality for various image features.

A novel adaptive mismatch cancellation system for quadrature IF radio receivers

Abstract: This paper investigates and resolves in-phase/quadrature phase (I/Q) imbalances between the input paths of quadrature IF receivers. These mismatches along the paths result in the image interference aliasing into the desired signal band, thus reducing the dynamic range and degrading the performance of the receivers. I/Q errors occur because of gain and phase imbalances between quadrature mixers. They are also caused by capacitor mismatches in analog-to-digital converters (A/Ds), which are designed to be identical for each input path. This paper presents a novel and feasible digital signal processing (DSP) solution for the I/Q mismatch problems. The system includes a novel complex least mean square algorithm and a modified adaptive noise canceler (signal separator) to separate the desired signal and the image noise caused by the mismatch. The noise canceler can also solve the signal leakage problem, which is that the noise reference includes signal components. This system was implemented in a Xilinx FPGA and an Analog Devices DSP chip. It was tested with a complex intermediate frequency receiver, which includes an analog front end and a complex sigma-delta modulator. Both simulation results and test results show a dramatic attenuation of the image noise. Extending applications of the system to N-path systems further indicates the robustness and feasibility of this novel adaptive mismatch cancellation system.

A new approach to subband adaptive filtering

Abstract: Subband adaptive filtering has attracted much attention lately. In this paper, we propose a new structure and a new formulation for adapting the filter coefficients. This structure is based on polyphase decomposition of the filter to be adapted and is independent of the type of filter banks used in the subband decomposition. The new formulation yields improved convergence rate when the LMS algorithm is used for coefficient adaptation. As we increase the number of bands in the filter, the convergence rate increases and approaches the rate that can be obtained with a flat input spectrum. The computational complexity of the proposed scheme is nearly the same as that of the fullband approach. Simulation results are included to demonstrate the efficacy of the new approach.

Feedback cancellation improvements

Abstract: Feedback cancellation apparatus uses a cascade of two filters along with a short bulk delay. The first filter is adapted when the hearing aid is turned on in the ear. This filter adapts quickly using a white noise probe signal, 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 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 shifts the filter response to make the most effective use of the limited number of filter coefficients.

On compensating nonlinear distortions of an OFDM system using an efficient adaptive predistorter

Abstract: This paper presents an efficient adaptive predistortion technique compensating for nonlinear distortions caused by a high-power amplifier (HPA) cascaded with a linear filter in an OFDM system. In the proposed approach, the memoryless HPA, preceded by a linear filter with memory in OFDM systems, is modeled by the Wiener system, which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. It is confirmed by computer simulation that the proposed approach produces a faster convergence speed than the previous adaptive predistortion technique, and provides a small output backoff as low as 5.5 dB for an OFDM system employing an HPA with a linear filter.

Removing Noise and Preserving Details with Relaxed Median Filters

Abstract: In this paper, a median based filter called relaxed median filter is proposed. The filter is obtained by relaxing the order statistic for pixel substitution. Noise attenuation properties as well as edge and line preservation are analyzed statistically. The trade-off between noise elimination and detail preservation is widely analyzed. It is shown that relaxed median filters preserve details better than the standard median filter, and remove noise better than other median type filters.

A new method for varying adaptive bandwidth selection

Abstract: A novel approach is developed to solve a problem of varying bandwidth selection for filtering a signal given with an additive noise. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and adaptive to unknown smoothness of the signal.

Robust estimation with unknown noise statistics

Abstract: The equivalence between the Kalman filter and a particular least squares regression problem is established and the regression problem is solved robustly using a statistical approach, named M-estimation. M-robust estimators are derived for adaptive estimation of the unknown a priori state and observation noise statistics simultaneously with the system states. The feasibility of the approach is demonstrated with simulation.

Adaptive fuzzy systems for multichannel signal processing

Abstract: Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing. The perspective of the topic offered here is one that comes primarily from work done in the field of multichannel (color) image processing. Hence, many of the techniques and works cited here relate to image processing with the emphasis placed primarily on filtering algorithms based on fuzzy concepts, multidimensional scaling, and order statistics-based designs. It should be noted, however, that multichannel signal processing is a very broad field and thus contains many other approaches that have been developed from different perspectives, such as transform domain filtering, classical least-square approaches, neural networks, and stochastic methods, just to name a few. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applications, such as color image processing, is illustrated with several examples.

Universal linear prediction by model order weighting

Abstract: A common problem that arises in adaptive filtering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error for any bounded individual sequence is as good as the performance attainable by the best sequential linear predictor of order less than some M. This predictor is found by transforming linear prediction into a problem analogous to the sequential probability assignment problem from universal coding theory. The resulting universal predictor uses essentially a performance-weighted average of all predictors for model orders less than M. Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order. Examples of prediction performance are provided for autoregressive and speech data as well as an example of adaptive data equalization.

Design of near perfect reconstruction oversampled filter banks for subband adaptive filters

Abstract: In this brief, a design algorithm for real-valued and complex-valued oversampled filter banks which yield a low level of inband alias and enable simple subband adaptive structures is presented. The filter banks are either based on complex modulation of a real-valued low-pass prototype or on the direct or modulated setups of real-valued filter banks. If real-valued filter banks are required, then the different channels will have different subsampling ratios so that the bandpass sampling theorem is not violated. This brief also presents design examples of real-valued and complex-valued filter banks.

Nonlinear acoustic echo cancellation with 2nd order adaptive Volterra filters

Abstract: Acoustic echo cancellers in today's speakerphones or video conferencing systems rely on the assumption of a linear echo path. Low-cost audio equipment or constraints of portable communication systems cause nonlinear distortions, which limit the echo return loss enhancement achievable by linear adaptation schemes. These distortions are a super-position of different effects, which can be modelled either as memoryless nonlinearities or as nonlinear systems with memory. Proper adaptation schemes for both cases of nonlinearities are discussed. An echo canceller for nonlinear systems with memory based on an adaptive second order Volterra filter is presented. Its performance is demonstrated by measurements with small loudspeakers. The results show an improvement in the echo return loss enhancement of 7 dB over a conventional linear adaptive filter. The additional computational requirement for the presented Volterra filter is comparable to that of existing acoustic echo cancellers.