Showing papers on "Adaptive filter published in 1991"

The design and use of steerable filters

Abstract: The authors present an efficient architecture to synthesize filters of arbitrary orientations from linear combinations of basis filters, allowing one to adaptively steer a filter to any orientation, and to determine analytically the filter output as a function of orientation. Steerable filters may be designed in quadrature pairs to allow adaptive control over phase as well as orientation. The authors show how to design and steer the filters and present examples of their use in the analysis of orientation and phase, angularly adaptive filtering, edge detection, and shape from shading. One can also build a self-similar steerable pyramid representation. The same concepts can be generalized to the design of 3-D steerable filters. >

Center weighted median filters and their applications to image enhancement

Abstract: The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the central value of each window, is studied. This filter can preserve image details while suppressing additive white and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM filter can outperform the median filter. Some relationships between CWM and other median-type filters, such as the Winsorizing smoother and the multistage median filter, are derived. In an attempt to improve the performance of CWM filters, an adaptive CWM (ACWM) filter having a space varying central weight is proposed. It is shown that the ACWM filter is an excellent detail preserving smoother that can suppress signal-dependent noise as well as signal-independent noise. >

Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection

Abstract: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy electrocardiogram (ECG), and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm. >

Adaptive polynomial filters

Abstract: 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. >

Adaptive smoothing: a general tool for early vision

Abstract: A method to smooth a signal while preserving discontinuities is presented. This is achieved by repeatedly convolving the signal with a very small averaging mask weighted by a measure of the signal continuity at each point. Edge detection can be performed after a few iterations, and features extracted from the smoothed signal are correctly localized (hence, no tracking is needed). This last property allows the derivation of a scale-space representation of a signal using the adaptive smoothing parameter k as the scale dimension. The relation of this process to anisotropic diffusion is shown. A scheme to preserve higher-order discontinuities and results on range images is proposed. Different implementations of adaptive smoothing are presented, first on a serial machine, for which a multigrid algorithm is proposed to speed up the smoothing effect, then on a single instruction multiple data (SIMD) parallel machine such as the Connection Machine. Various applications of adaptive smoothing such as edge detection, range image feature extraction, corner detection, and stereo matching are discussed. >

Design and implementation of an extended Kalman filter for the state estimation of a permanent magnet synchronous motor

Abstract: Practical considerations for implementing the discrete extended Kalman filter in real time with a digital signal processor are discussed. The system considered is a permanent magnet synchronous motor (PMSM) without a position sensor, and the extended Kalman filter is designed for the online estimation of the speed and rotor position by only using measurements of the motor voltages and currents. The algorithms developed to allow efficient computation of the filter are presented. The computational techniques used to simplify the filter equations and their implementation in fixed-point arithmetic are discussed. Simulation and experimental results are presented to demonstrate the feasibility of this estimation process. >

Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation

Abstract: The deterministic properties of weighted median (WM) filters are analyzed. Threshold decomposition and the stacking property together establish a unique relationship between integer and binary domain filtering. The authors present a method to find the weighted median filter which is equivalent to a stack filter defined by a positive Boolean function. Because the cascade of WM filters can always be expressed as a single stack filter this allows expression of the cascade of WM filters as a single WM filter. A direct application is the computation of the output distribution of a cascade of WM filters. The same method is used to find a nonrecursive expansion of a recursive WM filter. As applications of theoretical results, several interesting deterministic and statistical properties of WM filters are derived. >

An eigenanalysis interference canceler

Abstract: Eigenanalysis methods are applied to interference cancellation problems. While with common array processing methods the cancellation is effected by global optimization procedures that include the interferences and the background noise, the proposed technique focuses on the interferences only, resulting in superior cancellation performance. Furthermore, the method achieves full effectiveness even for short observation times, when the number of samples used for processing is of the the order of the number of interferences. Adaptive implementation is obtained with a simple, fast converging algorithm. >

Numerically stable fast transversal filters for recursive least squares adaptive filtering

Abstract: A solution is proposed to the long-standing problem of the numerical instability of fast recursive least squares transversal filter (FTF) algorithms with exponential weighting, an important class of algorithms for adaptive filtering. A framework for the analysis of the error propagation in FTF algorithms is first developed; within this framework, it is shown that the computationally most efficient 7N form is exponentially unstable. However, by introducing redundancy into this algorithm, feedback of numerical errors becomes possible; a judicious choice of the feedback gains then leads to a numerically stable FTF algorithm with a complexity of 8N multiplications and additions per time recursion. The results are presented for the complex multichannel joint-process filtering problem. >

An improved lattice-based adaptive IIR notch filter

Abstract: A novel lattice-based adaptive infinite impulse response (IIR) notch filter is developed which features independent tuning of the notch frequency and attenuation bandwidth. The internal structure is based on planar rotators, ensuring reliable numerical behaviour and high processing rates in CORDIC environments. A simple update law allows a simpler implementation than previously proposed designs. Rather than minimizing an output error cost function, the algorithm is designed to achieve a stable associated differential equation, resulting in a globally convergent unbiased frequency estimator in the single sinusoid case, independent of the notch filter bandwidth. Using a second-order structure in the multiple sinusoid case, unbiased estimation of one of the input frequencies is achieved by thinning the notch bandwidth. The tracking behavior is superior to conventional output error designs, and the estimation of extremal frequencies is less prone to overflow instability than previously reported structures. >

Adaptive equalization for TDMA digital mobile radio

Abstract: Adaptive equalization for a TDMA (time-division multiple-access) digital cellular system is discussed. A survey of adaptive equalization techniques that includes their performance characteristics and limitations and their implementation complexity is presented. The design of adaptive equalization algorithms for a narrowband TDMA system is considered. It is concluded that, on the basis of implementation complexity and performance in the presence of multipath distortion and signal fading, MLSE (maximum-likelihood sequence estimation) and DFE (decision feedback equalization) are viable equalization methods for mobile radio. >

A class of fast Gaussian binomial filters for speech and image processing

Abstract: The authors present an efficient, in-place algorithm for the batch processing of linear data arrays. These algorithms are efficient, easily scaled, and have no multiply operations. They are suitable as front-end filters for a bank of quadrature mirror filters and for pyramid coding of images. In the latter application, the binomial filter was used as the low-pass filter in pyramid coding of images and compared with the Gaussian filter devised by P.J. Burt (Comput. Graph. Image Processing, vol.16, p.20-51, 1981). The binomial filter yielded a slightly larger signal-to-noise ratio in every case tested. More significantly, for an (L+1)*(L+1) image array processed in (N+1)*(N+1) subblocks, the fast Burt algorithm requires a total of 2(L+1)/sup 2/N adds and 2(L+1)/sup 2/ (N/2+1) multiplies. The binomial algorithm requires 2L/sup 2/N adds and zero multiplies. >

The analysis and design of multidimensional FIR perfect reconstruction filter banks for arbitrary sampling lattices

Abstract: A general analysis of multidimensional multirate filter banks is presented. The approach is applicable to discrete signal spaces of any dimension, to multirate systems based on arbitrary downsampling and upsampling lattices, and for filter banks with any number of channels. A new numerical design procedure is also presented for multidimensional multirate perfect reconstruction filter banks, which is based on methods of nonlinearly constrained numerical optimization. An error function that depends only on the analysis filter impulse response coefficients is minimized, subject to a set of quadratic equality constraints that involve both the analysis and synthesis filter coefficients. With this design framework, it is possible to design a wide variety of filter banks that have a number of desirable properties. The analysis and synthesis filters that result are finite impulse response (FIR) and of equal size. In addition, both paraunitary and nonparaunitary filter banks can be designed with this method. Unlike paraunitary filter banks, nonparaunitary filter banks are capable of performing analysis bank functions more general than band-splitting with flat passband filters. >

Least squares restoration of multichannel images

Abstract: Multichannel restoration using both within- and between-channel deterministic information is considered. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using the set theoretic and the constrained optimization approaches. A geometric interpretation of the estimates of both filters is given. Color images (three-channel imagery with red, green, and blue components) are considered. Constraints that capture the within- and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Experiments using color images are described. >

Composite modeling for adaptive short-term load forecasting

Abstract: A composite load model is developed for predicting hourly electric loads 1-24 h ahead. The load model is composed of three components: the nominal load, the type load, and the residual load. The nominal load is modeled in such a way that the Kalman filter can be used, and the parameters of the model are adapted by the exponentially weighted recursive-least-squares method. The type load component is extracted for weekend load prediction and updated by an exponential smoothing method. The residual load is predicted by the autoregressive model, and the parameters of the model are estimated using the recursive-least-squares method. Test results are presented using utility data for two different years. >

Noise squelch circuit with adaptive noise shaping

Abstract: A noise squelch circuit for a radio receiver (100) includes an adaptive filter (204) for shaping frequency characteristics of a demodulator out put (115) according to factors which effects squelch sensitivity. Such factors may include channel spacing of the receiver, received signal strength level, received signal deviation, and SINAD. The adaptive filter (204) comprises a switched capacitor filter, the response of which may be controlled by a control signal (212) according to one or more of such factors.

On optimal infinite impulse response edge detection filters

Abstract: The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters, operating in two orthogonal directions. The implementation is very simple and computationally efficient. has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation. >

Iterative Wiener filters for image restoration

Abstract: The iterative Wiener filter, which successively uses the Wiener-filtered signal as an improved prototype to update the covariance estimates, is investigated. The convergence properties of this iterative filter are analyzed. It has been shown that this iterative process converges to a signal which does not correspond to the minimum mean-squared-error solution. Based on the analysis, an alternate iterative filter is proposed to correct for the convergence error. The theoretical performance of the filter has been shown to give minimum mean-squared error. In practical implementation when there is unavoidable error in the covariance computation, the filter may still result in undesirable restoration. Its performance has been investigated and a number of experiments in a practical setting were conducted to demonstrate its effectiveness. >

A new class of nonlinear filters for image enhancement

Abstract: Two types of very simple two-dimensional nonlinear filters are introduced and applied to image contrast enhancement. The first type is based on a generalization of the Teager's algorithm. A theoretical analysis has shown that this type of nonlinear filter works like a local-mean-weighted highpass filter. Based on this analysis, a second type of nonlinear filter has been developed which works like local-mean-weighted bandpass filter. The proposed image contrast enhancement technique is based on combining the original image with its filtered version obtained using one of the two nonlinear filters. Very high quality enhancement has been achieved for natural images. >

Adaptive linearization of a loudspeaker

Abstract: An application for adaptive nonlinear filters, adaptive linearization of a loudspeaker, is presented. An adaptive predistortion linearization scheme is used for linearizing a loudspeaker. A model of a direct radiator loudspeaker is developed and studied which takes into account two principal sources of nonlinear distortions: nonlinearity in the suspension system and inhomogeneity in the flux density. Based on this model, simulations of the proposed method were performed. The results show that nonlinear distortions of a loudspeaker can be reduced significantly. >

Feedback cancellation in hearing aids: results from a computer simulation

Abstract: Feedback cancellation in hearing aids involves estimating the feedback signal and subtracting it from the microphone input signal. The feedback-cancellation system described updates the estimated feedback path whenever changes are detected in the feedback behavior. When a change is detected, the normal hearing-aid processing is interrupted, a pseudorandom probe signal is injected into the system, and a set of filter coefficients is adjusted to give an estimate of the feedback path. The hearing aid is then returned to normal operation with the feedback-cancellation filter as part of the system. Two approaches are investigated for computing the filter coefficients: a least-mean square (LMS) adaptive filter and a Wiener filter. Test results are presented for a computer simulation of an in-the-ear (ITE) hearing aid. The simulation results indicate that more than 10 dB of cancellation can be obtained and that the Wiener filter is more effective in the presence of strong interference. >

Fast Newton transversal filters-a new class of adaptive estimation algorithms

Abstract: A class of adaptive algorithms for the estimation of FIR (finite impulse response) transversal filters is presented. The main characteristic of this class is the fast computation of the gain vector needed for the adaptation of the transversal filters. The method for deriving these algorithms is based on the assumption that the input signal is autoregressive of order M, where M can be much smaller than the order of the filter to be estimated. Under this assumption the covariance matrix of the input signal is estimated by extending in a min-max way the M order sample covariance matrix. This estimate can be regarded as a generalization of the diagonal covariance matrix used in LMS and leads to an efficient computation of the gain needed for the adaptation. The new class of algorithms contains the LMS and the fast versions of LS as special cases. The complexity changes linearly with M, starting from the complexity of the LMS (for M=0) and ending at the complexity of the fast versions of LS. >

Continuous-time LMS adaptive recursive filters

Abstract: An approach for implementing continuous-time adaptive recursive filters is presented. The resulting filters should be capable of operating on much higher signal frequencies than their digital counterparts since no sampling is required. With respect to implementation problems, the effects of DC offsets are investigated and formulas derived so that these effects can be estimated and reduced. It is shown that the DC offset performance is strongly affected by the choice of structure for the adaptive filter. Experimental results from a discrete prototype are given where accurate adaption is observed and DC offset effects are compared to theoretical predictions. >

Adaptive filtering and maximum entropy spectra with application to changes in atmospheric angular momentum

Abstract: The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.

On the duality between fast QR methods and lattice methods in least squares adaptive filtering

Abstract: The authors show that fast QR methods and lattice methods in least squares adaptive filtering are duals and follow from identical geometric principles. Whereas the lattice methods compute the residuals of a projection operation via the forward and backward prediction errors, the QR methods compute instead the weights used in the projections. Within this framework, the parameter identification problem is solved using fast QR methods by showing that the reflection coefficients and tap parameters of a least squares lattice filter operating in the joint process mode are immediately available as internal variables in the fast QR algorithms. This parameter set can be readily exploited in system identification, signal analysis, and linear predictive coding, for example. The relations derived also lead to a fast least squares algorithm of minimal complexity that is a hybrid between a QR and a lattice algorithm. The algorithm combines the order recursive properties of the lattice approach with the robust numerical behavior of the QR approach. >

Genetic algorithm in the design of FIR filters

Abstract: The author's aim is to describe a method of designing finite impulse response (FIR) filters that is automatic, rapid, and gives filter realisations of near minimal computational complexity. Existing methods of filter design are reviewed to show that none possesses all these features. These methods include recent work using a sequential algorithm that produces realisations of guaranteed minimum complexity and thus provides a reference for the results described in the paper. Genetic algorithms are described, and a method of representing the problem of filter synthesis for solution by a genetic algorithm is given. Results are presented, demonstrating the suitability of the genetic algorithm design method. >

Digital signal processing: theory, applications, and hardware

Abstract: Designed for graduate students and signal processing practitioners with an introductory background in DSP, this new text gives representative coverage of advanced topics (orthogonal expansions, optimal filters, and two-dimensional DSP), and advanced aspects of familiar topics (fast transforms beyond the FFT, non-uniform sampling and quantization). Providing a self-contained blending of DSP theory, applications to speech and image processing, and state-of-the-art DSP hardware, "Digital Signal Processing" includes: introductory DSP concepts summarized in five appendixes; DSP filter algorithms - e.g.subband and median filters; least squares, optimal, and adaptive filters spectral estimation and deconvolution; speech and image processing applications; and DSP hardware realizations.

Adaptive MLSE-VA receiver for digital cellular radio

Abstract: A TDMA RF received signal is demodulated by first being filtered with a fixed transversal filter having a characteristic selected for matching a fixed square root raised cosine pulse characteristic of the received signal. The filtered signal is then adaptively filtered for compensating for a time varying impulse response of the channel. The adaptive filtering is performed initially during a synchronizing portion (preamble) of the filtered signal in accordance with a fast recursive least squares algorithm. Subsequent filter adaptation to a data portion of the filtered signal is accomplished in accordance with a computationally less expensive normalized least mean square procedure. The adaptive filter repetitively applies a modified Viterbi algorithm to blocks of 2D symbols, such that D symbols are released for adapting the adaptive filter means during the data portion of the filtered signal and the signal. The released symbols are also employed for adapting elements required in computing a metric for the modified Viterbi algorithm and the reconstructed signal used to form an error signal that drives the adaptation algorithms.