Showing papers on "Adaptive filter published in 2007"

Vlsi Digital Signal Processing Systems: Design And Implementation

Abstract: Introduction to Digital Signal Processing Systems. Iteration Bound. Pipelining and Parallel Processing. Retiming. Unfolding. Folding. Systolic Architecture Design. Fast Convolution. Algorithmic Strength Reduction in Filters and Transforms. Pipelined and Parallel Recursive and Adaptive Filters. Scaling and Roundoff Noise. Digital Lattice Filter Structures. Bit-Level Arithmetic Architectures. Redundant Arithmetic. Numerical Strength Reduction. Synchronous, Wave, and Asynchronous Pipelines. Low-Power Design. Programmable Digital Signal Processors. Appendices. Index.

A Nonlinear Bayesian Filtering Framework for ECG Denoising

Abstract: In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.

A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise

Abstract: The known median-based denoising methods tend to work well for restoring the images corrupted by random-valued impulse noise with low noise level but poorly for highly corrupted images. This letter proposes a new impulse detector, which is based on the differences between the current pixel and its neighbors aligned with four main directions. Then, we combine it with the weighted median filter to get a new directional weighted median (DWM) filter. Extensive simulations show that the proposed filter not only can provide better performance of suppressing impulse with high noise level but can preserve more detail features, even thin lines. As extended to restoring corrupted color images, this filter also performs very well

An adaptive covariance inflation error correction algorithm for ensemble filters

Abstract: Ensemble filter methods for combining model prior estimates with observations of a system to produce improved posterior estimates of the system state are now being applied to a wide range of problems both in and out of the geophysics community. Basic implementations of ensemble filters are simple to develop even without any data assimilation expertise. However, obtaining good performance using small ensembles and/or models with significant amounts of error can be more challenging. A number of adjunct algorithms have been developed to ameliorate errors in ensemble filters. The most common are covariance inflation and localization which have been used in many applications of ensemble filters. Inflation algorithms modify the prior ensemble estimates of the state variance to reduce filter error and avoid filter divergence. These adjunct algorithms can require considerable tuning for good performance, which can entail significant expense. A hierarchical Bayesian approach is used to develop an adaptive covariance inflation algorithm for use with ensemble filters. This adaptive error correction algorithm uses the same observations that are used to adjust the ensemble filter estimate of the state to estimate appropriate values of covariance inflation. Results are shown for several low-order model examples and the algorithm produces results that are comparable with the best tuned inflation values, even for small ensembles in the presence of very large model error.

A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter

Abstract: A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.

Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm

Abstract: The adaptive nonlinear predistorter is an effective technique to compensate for the nonlinear distortion existing in digital communication and control systems. However, available adaptive nonlinear predistorters using indirect learning are sensitive to measurement noise and do not perform optimally. Other available types are either slow to converge, complicated in structure and computationally expensive, or do not consider the memory effects in nonlinear systems such as a high power amplifier (HPA). In this paper, we first propose several novel adaptive nonlinear predistorters based on direct learning algorithms - the nonlinear filtered-x RLS (NFXRLS) algorithm, the nonlinear adjoint LMS (NALMS) algorithm, and the nonlinear adjoint RLS (NARLS) algorithm. Using these new learning algorithms, we design adaptive nonlinear predistorters for an HPA with memory effects or for an HPA following a linear system. Because of the direct learning algorithm, these novel adaptive predistorters outperform nonlinear predistorters that are based on the indirect learning method in the sense of normalized mean square error (NMSE), bit error rate (BER), and spectral regrowth. Moreover, our developed adaptive nonlinear predistorters are computationally efficient and/or converge rapidly when compared to other adaptive nonlinear predistorters that use direct learning, and furthermore can be easily implemented. We further simplify our proposed algorithms by exploring the robustness of our proposed algorithm as well as by examining the statistical properties of what we call the "instantaneous equivalent linear" (IEL) filter. Simulation results confirm the effectiveness of our proposed algorithms

Reduced-Rank Adaptive Filtering Based on Joint Iterative Optimization of Adaptive Filters

Abstract: This letter proposes a novel adaptive reduced-rank filtering scheme based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that forms the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and low-complexity normalized least-mean squares (NLMS) adaptive algorithms for its efficient implementation. Simulations for an interference suppression application show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at significantly lower complexity.

Adaptive vertex fitting

Abstract: Vertex fitting frequently has to deal with both mis-associated tracks and mis-measured track errors. A robust, adaptive method is presented that is able to cope with contaminated data. The method is formulated as an iterative re-weighted Kalman filter. Annealing is introduced to avoid local minima in the optimization. For the initialization of the adaptive filter a robust algorithm is presented that turns out to perform well in a wide range of applications. The tuning of the annealing schedule and of the cut-off parameter is described using simulated data from the CMS experiment. Finally, the adaptive property of the method is illustrated in two examples.

Direction-Adaptive Discrete Wavelet Transform for Image Compression

Abstract: We propose a direction-adaptive DWT (DA-DWT) that locally adapts the filtering directions to image content based on directional lifting. With the adaptive transform, energy compaction is improved for sharp image features. A mathematical analysis based on an anisotropic statistical image model is presented to quantify the theoretical gain achieved by adapting the filtering directions. The analysis indicates that the proposed DA-DWT is more effective than other lifting-based approaches. Experimental results report a gain of up to 2.5 dB in PSNR over the conventional DWT for typical test images. Subjectively, the reconstruction from the DA-DWT better represents the structure in the image and is visually more pleasing

Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal

Abstract: Partial Differential equations (PDE), wavelets-based methods and neighborhood filters were proposed as locally adaptive machines for noise removal Recently, Buades, Coll and Morel proposed the Non-Local (NL-) means filter for image denoising This method replaces a noisy pixel by the weighted average of other image pixels with weights reflecting the similarity between local neighborhoods of the pixel being processed and the other pixels The NL-means filter was proposed as an intuitive neighborhood filter but theoretical connections to diffusion and non-parametric estimation approaches are also given by the authors In this paper we propose another bridge, and show that the NL-means filter also emerges from the Bayesian approach with new arguments Based on this observation, we show how the performance of this filter can be significantly improved by introducing adaptive local dictionaries and a new statistical distance measure to compare patches The new Bayesian NL-means filter is better parametrized and the amount of smoothing is directly determined by the noise variance (estimated from image data) given the patch size Experimental results are given for real images with artificial Gaussian noise added, and for images with real image-dependent noise

Estimation of Power System Frequency Using an Adaptive Notch Filter

Abstract: An algorithm based on the concept of adaptive notch filter (ANF) is proposed for estimation of power system frequency. The ANF is a second-order notch filter that is further furnished with a nonlinear differential equation to update the frequency. The method permits direct estimation of frequency and its rate of change for a power system signal. The performance of the algorithm is compared with that of a newly introduced algorithm, which is based on using an enhanced phase-locked loop (PLL) system. Unlike the PLL-based frequency estimator, the proposed algorithm does not employ a voltage-controlled oscillator. This makes its structure much simpler for implementation. The transient response of the proposed estimator is faster than that of the PLL-based estimator. Computer simulations are presented to highlight the usefulness of this approach in estimating near-nominal and off-nominal power system frequencies.

Artifact removal from EEG signals using adaptive filters in cascade

Abstract: Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

Novel data association schemes for the probability hypothesis density filter

Abstract: The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.

Time-Domain Signal Analysis Using Adaptive Notch Filter

Abstract: Noise reduction and signal decomposition are among important and practical issues in time-domain signal analysis. This paper presents an adaptive notch filter (ANF) to achieve both these objectives. For noise reduction purpose, the proposed adaptive filter successfully extracts a single sinusoid of a possibly time-varying nature from a noise-corrupted signal. The paper proceeds with introducing a chain of filters which is capable of estimating the fundamental frequency of a signal composed of harmonically related sinusoids, and of decomposing it into its constituent components. The order of differential equations governing this algorithm is 2n+1, where n is the number of constituent sinusoids that should be extracted. Stability analysis of the proposed algorithm is carried out based on the application of the local averaging theory under the assumption of slow adaptation. When compared with the conventional Fourier analysis, the proposed method provides instantaneous values of the constituting components. Moreover, it is adaptive with respect to the fundamental frequency of the signal. Simulation results verify the validity of the presented algorithm and confirm its desirable transient and steady-state performances as well as its desirable noise characteristics

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches

Abstract: to Nonlinear Systems.- Polynomial Models of Nonlinear Systems.- Volterra and Wiener Nonlinear Models.- Nonlinear System Identification Methods.- to Adaptive Signal Processing.- Nonlinear Adaptive System Identification Based on Volterra Models.- Nonlinear Adaptive System Identification Based on Wiener Models (Part 1).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 2).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 3).- Nonlinear Adaptive System Identification Based on Wiener Models (Part 4).- Conclusions, Recent Results, and New Directions.

Compressed Sensing Image Reconstruction Via Recursive Spatially Adaptive Filtering

Abstract: We introduce a new approach to image reconstruction from highly incomplete data. The available data are assumed to be a small collection of spectral coefficients of an arbitrary linear transform. This reconstruction problem is the subject of intensive study in the recent field of "compressed sensing" (also known as "compressive sampling"). Our approach is based on a quite specific recursive filtering procedure. At every iteration the algorithm is excited by injection of random noise in the unobserved portion of the spectrum and a spatially adaptive image denoising filter, working in the image domain, is exploited to attenuate the noise and reveal new features and details out of the incomplete and degraded observations. This recursive algorithm can be interpreted as a special type of the Robbins-Monro stochastic approximation procedure with regularization enabled by a spatially adaptive filter. Overall, we replace the conventional parametric modeling used in CS by a nonparametric one. We illustrate the effectiveness of the proposed approach for two important inverse problems from computerized tomography: Radon inversion from sparse projections and limited-angle tomography. In particular we show that the algorithm allows to achieve exact reconstruction of synthetic phantom data even from a very small number projections. The accuracy of our reconstruction is in line with the best results in the compressed sensing field.

Adaptive Filtering for Color Filter Array Demosaicking

Abstract: Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.

Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control

Abstract: In this paper, we treat nonlinear active noise control (NANC) with a linear secondary path (LSP) and with a nonlinear secondary path (NSP) in a unified structure by introducing a new virtual secondary path filter concept and using a general function expansion nonlinear filter. We discover that using the filtered-error structure results in greatly reducing the computational complexity of NANC. As a result, we extend the available filtered-error-based algorithms to solve NANC/LSP problems and, furthermore, develop our adjoint filtered-error-based algorithms for NANC/NSP. This family of algorithms is computationally efficient and possesses a simple structure. We also find that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP. Finally, we use the statistical properties of the virtual secondary path and the robustness of our proposed methods to further reduce the computational complexity and simplify the implementation structure of NANC/NSP when the NSP satisfies certain conditions. Computational complexity and simulation results are given to confirm the efficiency and effectiveness of all of our proposed methods

Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow

Abstract: Conventionally, most traffic forecasting models have been applied in a static framework in which new observations are not used to update model parameters automatically. The need to perform periodic parameter reestimation at each forecast location is a major disadvantage of such models. From a practical standpoint, the usefulness of any model depends not only on its accuracy but also on its ease of implementation and maintenance. This paper presents an adaptive parameter estimation methodology for univariate traffic condition forecasting through use of three well-known filtering techniques: the Kalman filter, recursive least squares, and least mean squares. Results show that forecasts obtained from recursive adaptive filtering methods are comparable with those from maximum likelihood estimated models. The adaptive methods deliver this performance at a significantly lower computational cost. As recursive, self-tuning predictors, the adaptive filters offer plug-and-play capability ideal for implementation in...

Blind Calibration of Timing Offsets for Four-Channel Time-Interleaved ADCs

Abstract: In this paper, we describe a blind calibration method for timing mismatches in a four-channel time-interleaved analog-to-digital converter (ADC). The proposed method requires that the input signal should be slightly oversampled. This ensures that there exists a frequency band around the zero frequency where the Fourier transforms of the four ADC subchannels contain only three alias components, instead of four. Then the matrix power spectral density (PSD) of the ADC subchannels is rank deficient over this frequency band. Accordingly, when the timing offsets are known, we can construct a filter bank that nulls the vector signal at the ADC outputs. We employ a parametrization of this filter bank to develop an adaptive null steering algorithm for estimating the ADC timing offsets. The null steering filter bank employs seven fixed finite-impulse response filters and three unknown timing offset parameters which are estimated by using an adaptive stochastic gradient technique. A convergence analysis is presented for the blind calibration method. Numerical simulations for a bandlimited white noise input and for inputs containing several sinusoidal components demonstrate the effectiveness of the proposed technique

Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal

Abstract: In this paper, we present an adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. Our new approach to slope restoration significantly differs from the previous slope restoration algorithms in that ABF does not involve detecting edge orientations or edge profiles. Compared with the bilateral filter, ABF restored images are significantly sharper. Compared with an unsharp mask (USM) based sharpening method the optimal USM (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without halo. ABF also outperforms the bilateral filter and the OUM in noise removal.

A New Active Noise Control Algorithm That Requires No Secondary Path Identification Based on the SPR Property

Abstract: Active noise control (ANC) has been widely applied in industry to reduce environmental noise and equipment vibrations. Most available control algorithms require the identification of the secondary path, which increases the control system complexity, contributes to an increased residual noise power, and can even cause the control system to fail if the identified secondary path is not sufficiently close to the actual path. In this paper, based on the geometric analysis and the strict positive real (SPR) property of the filtered-x LMS algorithm, we introduce a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases. We are able to extend the developed ANC algorithm to the case of active control of broadband noises through our use of a subband implementation of the ANC algorithm. Compared to other available control algorithms that do not require secondary path identification, our developed method is simple to implement, yields good performance, and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm

Fuzzy Weighting-Dependent Approach to $H_{\infty}$ Filter Design for Time-Delay Fuzzy Systems

Abstract: This correspondence is concerned with the Hinfin filter design for Takagi-Sugeno (T-S) fuzzy systems with state-delay. A delay-dependent design method is proposed in terms of linear matrix inequalities (LMI). The main contribution is the use of fuzzy weighting-dependent Lyapunov functionals which can reduce the conservatism arisen from the quadratic Lyapunov functional approach. An illustrative example is given to show the effectiveness of the present method

Active noise reduction adaptive filter leakage adjusting

Abstract: An active noise reduction system using adaptive filters. A method of operation the active noise reduction system includes smoothing a stream of leakage factors. The frequency of a noise reduction signal may be related to the engine speed of an engine associated with the system within which the active noise reduction system is operated. The engine speed signal may be a high latency signal and may be obtained by the active noise reduction system over audio entertainment circuitry.

Adaptive Processing over Distributed Networks

Abstract: The article describes recent adaptive estimation algorithms over distributed networks. The algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.

Two-Pole and Multi-Pole Notch Filters: A Computationally Effective Solution for GNSS Interference Detection and Mitigation

Abstract: In a Global Navigation Satellite System (GNSS) receiver the presence of detection and mitigation units, capable of reducing the impact of disturbing signals, can extremely enhance the position accuracy. However the presence of such units is usually limited to professional receivers that dispose of additional computational power that can be used for interference detection and mitigation. In this paper the two-pole notch filter, the natural extension of the one-pole notch filter analyzed in [1], is proposed as computationally effective solution for interference detection and mitigation. The notch filter structure and the adaptive algorithm employed for tracking the disturbing signal are analyzed, and an interference detection unit, based on the adaptive algorithm convergence, is proposed. The two-pole notch filter coupled with the detection unit is used as elementary block for the design of a multi-pole notch filter that can efficiently mitigate more than one CW interference. Theoretical and simulative analyses show the feasibility and the good performance of the proposed method.

Diffusion Least-Mean Squares Over Adaptive Networks

Abstract: Distributed adaptive algorithms are proposed to address the problem of estimation in distributed networks. We extend recent work by relying on static and adaptive diffusion strategies. The resulting adaptive networks are robust to node and link failures and present a substantial improvement over the non-cooperative case asserting that cooperation improves estimation performance. The distributed algorithms are peer-to-peer implementations suitable for networks with general topologies.