Showing papers on "Adaptive filter published in 1980"
09 Apr 1980
TL;DR: This paper discusses a family of filters that have been designed for Quadrature Mirror Filter (QMF) Banks that provide a significant improvement over conventional optimal equiripple and window designs when used in QMF banks.
Abstract: This paper discusses a family of filters that have been designed for Quadrature Mirror Filter (QMF) Banks. These filters provide a significant improvement over conventional optimal equiripple and window designs when used in QMF banks. The performance criterion for these filters differ from those usually used for filter design in a way which makes the usual filter design techniques difficult to apply. Two filters are actually designed simultaneously, with constraints on the stop band rejection, transition band width, and pass and transition band performance of the QMF filter structure made from those filters. Unlike most filter design problems, the behavior of the transition band is constrained, which places unusual requirements on the design algorithm. The requirement that the overall passband behavior of the QMF bank be constrained (which is a function of the passband and stop band behavior of the filter) also places very unusual requirements on the filter design. The filters were designed using a Hooke and Jeaves optimization routine with a Hanning window prototype. Theoretical results suggest that exactly flat frequency designs cannot be created for filter lengths greater than 2, however, using the discussed procedure, one can obtain QMF banks with as little as ±.0015dB ripple in their frequency response. Due to the nature of QMF filter applications, a small set of filters can be derived which will fit most applications.
724 citations
01 Dec 1980
TL;DR: In this paper, the problem of identifying and controlling when plant output is corrupted with disturbance and when plant parameters vary with time is considered, and the error model can be described by a non-homogeneous differential equation.
Abstract: Identification and control when plant output is corrupted with disturbance and when plant parameters vary with time are considered. It is shown how the error model can be described by a non-homogeneous differential equation. Subject to bounded disturbance and parameter variation and sufficiently rich input the parameter error vector remains bounded and explicit bounds are derived. For inputs that are not sufficiently rich some sufficient conditions for stability and some examples of instability are given. Finally, a non-linear adaptation algorithm is presented for the control problem that insures the boundedness of the parameter error vector and all signals in the system.
403 citations
TL;DR: An analysis of this technique is extended to the case when a linear filter appears in the auxiliary signal path and a general solution to this problem is obtained.
Abstract: A technique known as a "multiple correlation cancellation loop" and also as the "LMS algorithm" is widely used in adaptive arrays for radar, sonar, and communications, as well as in many other signal processing applications. In this paper an analysis of this technique is extended to the case when a linear filter appears in the auxiliary signal path. A general solution to this problem is obtained and several examples for narrow-band and broad-band signals are presented.
395 citations
TL;DR: In this paper, a frequency domain implementation of the LMS adaptive transversal filter is proposed, which requires less computation than the conventional LMS filter when the filter length equals or exceeds 64 sample points.
Abstract: A frequency domain implementation of the LMS adaptive transversal filter is proposed. This fast LMS (FLMS) adaptive filter requires less computation than the conventional LMS adaptive filter when the filter length equals or exceeds 64 sample points.
350 citations
TL;DR: In this article, the results from an application of a conceptual hydrologic model, combined with filtering and statistical estimation methods, to real-time forecasting of river discharges are very encouraging.
Abstract: The results from an application of a conceptual hydrologic model, combined with filtering and statistical estimation methods, to real-time forecasting of river discharges are very encouraging. The use of feedback significantly improves the overall forecasting capability of the model even when the model and input error statistics are not perfectly known. Identification of these statistics through adaptive filtering techniques is practical and further improves the performance of the model. Comparison with a simple linear adaptive ‘black box’ model is very favorable for the conceptual hydrologic model, especially for forecast lead times comparably to the response time of the catchment. The results emphasize the importance of using a realistic model of uncertainty accounting for the nonstationarity in the rainfall-runoff process.
326 citations
09 Apr 1980
TL;DR: An analysis of this technique is extended to the case when a linear filter appears in the auxiliary signal path and a general solution to this problem is obtained.
Abstract: A technique known as a "multiple correlation cancellation loop" and also as the "LMS algorithm" is widely used in adaptive arrays for radar, sonar, and communications, as well as in many other signal processing applications. In this paper, an analysis of this technique is extended to the case when a linear filter appears in the auxiliary signal path. A general solution to this problem is obtained and several examples for narrowband and broad-band signals are presented.
219 citations
TL;DR: A class of adaptive algorithms designed for use with IIR digital filters which offer a much reduced computational load for basically the same performance, and have their basis in the theory of hyperstability, which yields HARF, a hyperstable adaptive recursive filtering algorithm which has provable convergence properties.
Abstract: The concept of adaptation in digital filtering has proven to be a powerful and versatile means of signal processing in applications where precise a priori filter design is impractical. Adaptive filters have traditionally been implemented with FIR structures, making their analysis fairly straightforward but leading to high computation cost in many cases of practical interest (e.g, sinusoid enhancement). This paper introduces a class of adaptive algorithms designed for use with IIR digital filters which offer a much reduced computational load for basically the same performance. These algorithms have their basis in the theory of hyperstability, a concept historically associated with the analysis of closed-loop nonlinear time-varying control systems. Exploiting this theory yields HARF, a hyperstable adaptive recursive filtering algorithm which has provable convergence properties. A simplified version of the algorithm, called SHARF, is then developed which retains provable convergence at low convergence rates and is well suited to real-time applications. In this paper both HARF and SHARF are described and some background into the meaning and utility of hyperstability is given, in addition, computer simulations are presented for two practical applications of IIR adaptive filters: noise and multi-path cancellation.
187 citations
TL;DR: In this article, linear programming techniques were used to determine the optimal filter weights for minimizing the peak range sidelobes of a binary phase-coded waveform, and the resulting filter was compared with the filter obtained by use of the least square approximation to the ideal inverse filter.
Abstract: Linear programming techniques are utilized to determine the optimal filter weights for minimizing the peak range sidelobes of a binary phase-coded waveform. The resulting filter is compared with the filter obtained by use of the least square approximation to the ideal inverse filter. For a test case using the 13-element Barker code the linear programming filter is found to have peak sidelobes as much as 5 dB lower than the least squares filter of the same length.
108 citations
TL;DR: In this article, a general class of linear, time-invariant multivariable systems that can be used in block implementations of discrete-time filters are described, including an explicit expression for the matrix transfer function of the block processor in terms of the single-input, single-output filter transfer function.
Abstract: This paper describes the general class of linear, time-invariant multivariable systems that can be used in block implementations of time-invariant discrete-time filters. Explicit relations between the properties of the block processor and the properties of the implemented filter are derived, including an explicit expression for the matrix transfer function of the block processor in terms of the single-input, single-output filter transfer function. These properties and relations are independent of the form of realization of the block processor. It is shown that all irreducible state-space realizations of the block processor can be derived by a simple procedure from a simple realization of the required filter transfer function.
102 citations
TL;DR: The purpose of this correspondence is to introduce an adaptive algorithm for recursive filters, which are implemented via a lattice structure, so that stability can be achieved during the adaptation process.
Abstract: The purpose of this correspondence is to introduce an adaptive algorithm for recursive filters, which are implemented via a lattice structure. The motivation for doing so is that stability can be achieved during the adaptation process. For convenience, the corresponding algorithm is referred to as an "adaptive lattice algorithm" for recursive filters. Results pertaining to using this algorithm in a system-identification experiment are also included.
93 citations
TL;DR: In this paper, the generalized transfer function of a shift-variant digital filter was investigated. And the frequency characteristic of a digital filter in terms of generalized transfer functions was discussed.
Abstract: The paper considers filters described by linear shift-variant difference (LSV) equations. We present the notion of a generalized transfer function and discuss the frequency characteristic of a shift-variant digital filter in terms of the generalized transfer function. A method is presented for determining LSV difference equations from a certain class of impulse responses, and vice versa. In addition, some properties of the impulse response and the generalized transfer function of a shift-variant system are investigated in the present work.
TL;DR: In this paper, the transient behavior of the LMS adaptive filter was studied when configured as an adaptive line enhancer operating in the presence of a fixed or variable complex frequency sine-wave signal buried in white noise.
Abstract: The transient behavior of the LMS adaptive filter is studied when configured as an adaptive line enhancer operating in the presence of a fixed or variable complex frequency sine-wave signal buried in white noise. For a fixed frequency signal, the mean weights are shown to respond to signal more rapidly than to noise alone. For a chirped signal, a fixed parameter matrix first-order difference equation is derived for the mean weights and a closed-form steady-state solution obtained. The transient response is obtained as a function of the eigenvectors and eigenvalues of the input covariance matrix. Sufficient conditions for the stability of the transient response are derived and an upper bound on the eigenvalues obtained. Finally, the mean-square error is evaluated when responding to a chirped signal. A gain coefficient of the LMS algorithm is determined which minimizes the mean-square error for chirped signals as a function of chirp rate and signal and noise powers.
Book•
29 Apr 1980
TL;DR: A concise introduction to digital filtering, filter design and applications in the form of Kalman and Wiener filters, and the graphic method for frequency response computations, FIR filter design by the frequency sampling technique, equiripple FIRfilter design, circular convolution and an introduction to multirate digital filters.
Abstract: A concise introduction to digital filtering, filter design and applications in the form of Kalman and Wiener filters. Each subject is developed gradually with the help of worked examples. Covers both the theory of digital filters and their use in extracting information from noisy data. New to this edition: the graphic method for frequency response computations, FIR filter design by the frequency sampling technique, equiripple FIR filter design, circular convolution and an introduction to multirate digital filters.
09 Apr 1980
TL;DR: It is shown that a nonlinear extension of the conventional tapped delay line filter is amenable to adaptation with the LMS algorithm, and can be used to represent a class of nonlinear systems.
Abstract: The purpose of this paper is to describe a nonlinear system structure that can be used to perform adaptive interference cancellation. It is shown that a nonlinear extension of the conventional tapped delay line filter is amenable to adaptation with the LMS algorithm, and can be used to represent a class of nonlinear systems. Experimental results are presented illustrating cases where nonlinear interference cancelling is superior to conventional linear cancelling. The generalization of the conventional linear structure to the nonlinear one described appears to provide a significant performance advantage in certain cases. At the same time, the complicated analyses usually associated with optimal nonlinear systems and the requirement to measure higher-order statistical parameters are avoided by virtue of the LMS algorithm.
TL;DR: In this paper, the dynamic range of the state variables is independent of block length, roundoff noise decreases with block length and, if the filter is realized in normal form, all autonomous limit cycles can be eliminated.
Abstract: Finite word effects are examined for a class of block realizations that are derived from a state-space realization of the transfer function. For this class of block realizations it is found that the dynamic range of the state variables is independent of block length, roundoff noise decreases with block length and, if the filter is realized in normal form, all autonomous limit cycles can be eliminated.
01 Mar 1980
TL;DR: In this paper, two new methods are presented for the estimation of the frequencies of closely spaced complex valued sinusoidal signals in the presence of noise, and the most effective method is a computationally efficient method for realization of maximum likelihood or maximum posterior probability estimates of the frequency.
Abstract: Two new methods are presented for the estimation of the frequencies of closely spaced complex valued sinusoidal signals in the presence of noise. The most effective method is a computationally efficient method for realization of maximum likelihood or maximum posterior probability estimates of the frequencies. The second method is a class of algorithms for removing some of the deficiencies of present adaptive filtering and correlation-estimation approaches to estimation of frequencies, such as the forward-backward linear prediction method. In both of these new methods one is fitting a signal model to data. In method 1 the data are the observed samples of two complex sinusoids plus noise. In the second method the data are elements of an estimated correlation matrix, or of some of its eigenvectors, obtained from the observed samples.
TL;DR: In this article, a delta modulation-like sampled analog filter structure for realizing low-pass filters is described, which uses only the coefficients 0, + 1, and -1 and can be fabricated as a programmable CCD filter.
Abstract: A delta modulation-like sampled analog filter structure for realizing low-pass filters is described. The filter uses only the coefficients 0, + 1 , and -1 and can be fabricated as a programmable CCD filter. Interpolation and decimation are employed to increase the accuracy of the delta modulation. It is shown that with this scheme a given response can be realized arbitrarily closely. Examples are included.
TL;DR: In this article, an adaptive filtering methodology is proposed to account for transient errors in the prediction of the effective rainfall in real-time river discharge forecasting models under widely different wet and dry regimes, producing sequences of highly nonstationary prediction residuals.
Abstract: Real-time river discharge forecasting models operate under widely different wet and dry regimes, producing sequences of highly nonstationary prediction residuals. Errors in the mean areal precipitation estimation are often magnified due to model approximations and nonlinear behavior and result in significant effective input errors which propagate as transient signals through the system. Normal operation of a feedback scheme based on the Kalman filter with stationary error statistics cannot account for such discrepancies leading to local divergence between expected and observed prediction residuals. An adaptive filtering methodology which explicitly accounts for transient errors in the prediction due to errors in the estimation of the effective rainfall is presented. The filter operates normally under the assumption of only stationary or slowly varying noise, while a generalized likelihood ratio test is used to detect the presence of transient errors. When such errors are detected, estimates of their timing and magnitude are obtained, and the forecasts are appropriately corrected. An illustrative example of the procedure operation is given.
TL;DR: The purpose of this paper is to introduce a novel Gram-Schmidt orthogonalization predictor realization, and to present an adaptive algorithm to update its coefficients (weights), along with corresponding results obtained via some existing adaptive predictor algorithms.
Abstract: The purpose of this paper is to introduce a novel Gram-Schmidt orthogonalization predictor realization, and also to present an adaptive algorithm to update its coefficients (weights). Experimental results pertaining to this algorithm are included, along with corresponding results obtained via some existing adaptive predictor algorithms.
TL;DR: In this article, a continuously adaptive two-dimensional Kalman tracking filter for a low data rate track-while-scan (TWS) operation is introduced which enhances the tracking of maneuvering targets.
Abstract: A continuously adaptive two-dimensional Kalman tracking filter for a low data rate track-while-scan (TWS) operation is introduced which enhances the tracking of maneuvering targets. The track residuals in each coordinate, which are a measure of track quality, are sensed, normalized to unity variance, and then filtered in a single-pole filter. The magnitude Z of the output of this single-pole filter, when it exceeds a threshold Z1 is used to vary the maneuver noise spectral density q in the Kalman filter model in a continuous manner. This has the effect of increasing the tracking filter gains and containing the bias developed by the tracker due to the maneuvering target. The probability of maintaining track, with reasonably sized target gates, is thus increased, The operational characteristic of q versus Z assures that the tracker gains do not change unless there is high confidence that a maneuver is in progress.
Patent•
24 Mar 1980TL;DR: An adaptive signal processing system for enhancing the signal-to-interfere characteristics on both receive and transmit using an in phase-quadrature correlator to control phase and amplitude adjust circuits located in the antenna signal paths is described in this article.
Abstract: An adaptive signal processing system for enhancing the signal-to-interfere characteristics on both receive and transmit using an in phase-quadrature correlator to control phase and amplitude adjust circuits located in the antenna signal paths. The amplitude adjustment provides amplitude balance control with variable delay lines between a quadrature hybrid and a sum-difference hybrid. The analog outputs of the correlator are digitized to control incrementally adjustable delay lines of the phase and amplitude circuits in binary steps.
01 Jan 1980
TL;DR: This paper surveys sequential filter adaptation techniques and some applications for transversal FIR, lattice and recursive filters, which span a wide spectrum of possible performance/complexity tradeoffs.
Abstract: Over the past few years a number of new adaptive filter algorithms have been developed and applied to meet demands for faster convergence and better tracking properties than earlier techniques could offer Applications include adaptive channel equalization, adaptive predictive speech coding and on-line system identification This paper surveys sequential filter adaptation techniques and some applications for transversal FIR, lattice and recursive filters The available techniques fit into two main categories: (1) gradient-type methods (exemplified by the well-known LMS algorithm) in which successive corrections to adaptive system parameters are only correct in an average sense, and (2) recursive least-squares methods, which continuously provide the solution to a numerical optimization problem, given all the preceding data The available techniques span a wide spectrum of possible performance/complexity tradeoffs
TL;DR: The performance and learning characteristics of the continuously adaptive lattice form for prediction-error filtering, and application of the filter to the problem of radar clutter discrimination is presented and discussed.
Abstract: This paper describes the performance and learning characteristics of the continuously adaptive lattice form for prediction-error filtering. Quantitative relationships are developed for convergence behavior, and procedures are described for selection of the adaptive weighting constant and filter order. Burg's algorithm is used to calculate the reflection coefficients of the filter. Based on this algorithm, two recursive relationships are developed to calculate the coefficients iteratively, one form assuming a stationary input signal, and a more complex form not making this assumption. A quantitative exposition of the convergence behavior in terms of an adaptive weighting constant is set down for these relationships for the first-order filter. Careful attention is given to the decoupling of higher filter orders, leading to the creation of a decoupling constant for the stationary signal case. Higher order convergence and the factors affecting it are examined, resulting in a procedure for choosing the adaptive weighting constant based on the input signal characteristics. Properties of the filter in the spectral domain are also examined. This leads to selection criteria for choosing the filter order, based on the signal characteristics. Application of the filter to the problem of radar clutter discrimination is presented and discussed.
TL;DR: In this paper, a natural generalization is presented which allows the method to be used to design arbitrary zero-phase 2-D FIR filters, and the decoupling of the design problem into one-dimensional and two-dimensional components.
Abstract: The design of 2-D FIR zero-phase filters by transformations is a popular and well-developed technique, but it suffers from the disadvantage that in its current form only filters with four-quadrant symmetry can be designed. In this contribution a natural generalization is presented which allows the method to be used to design arbitrary zero-phase 2-D FIR filters. This generalization preserves the two most important features of the method-the decoupling of the design problem into one-dimensional and two-dimensional components, and the existence of an efficient structure for realizing the filter.
Patent•
15 Feb 1980
TL;DR: In this paper, a plurality of transversal filters are disposed in front of a linear combiner for each diversity channel, and the tap gains of each filter are updated to estimate the sampled values of the channel impulse response for each diverse channel.
Abstract: An adaptive diversity receiver for digital communications provides a plurality of transversal filters disposed in front of a linear combiner for each of a plurality of diverse channels. The tap gains of each of the transversal filters are updated to estimate the sampled values of the channel impulse response for each diverse channel as a function of the detected data output, rather than providing tap gain updating as a function of the data error signal. In this manner, each transversal filter operates as an adaptive match filter for the corresponding diversity channel.
TL;DR: A comparison of the three methods shows that the adaptive lattice structure has faster convergence than the adaptive tapped delay line structure and the adaptive Kalman-filter identifier is found to be better than both the others, although it is computationally more complex.
Abstract: The results of application of three different methods to the "adaptive deconvolution" of seismic data are reported here. These are based on the adaptive tapped delay line filter, the adaptive lattice filter and the adaptive Kalman-filter identifier. All the three methods are shown to be superior to the "fixed-structure" predictor. A comparison of the three methods shows that the adaptive lattice structure has faster convergence than the adaptive tapped delay line structure. The adaptive Kalman filter identifier is found to be better than both the others, although it is computationally more complex. The conclusions are based both on theoretical studies as well as on experiments with real seismic data.
TL;DR: In this article, an optimal recursive digital filter design algorithm is presented to meet simultaneous specifications of magnitude and group delay responses, which is the unconstrained least pth optimization method for a constrained nonlinear programming problem.
Abstract: This paper presents a new optimal recursive digital filter design algorithm to meet simultaneous specifications of magnitude and group delay responses. The technique used is the unconstrained least pth optimization method for a constrained nonlinear programming problem. An illustrative example shows the drastic reduction of group delay ripple in comparison with the all-pass equalizer method.
01 Apr 1980
TL;DR: By improving the estimate of the steady-state mean square error (MSE), a tighter stability constraint is obtained, as well as more accurate expressions for the SNR gain attained by the adaptive line enhancer when filtering sinusoidal signals in white noise.
Abstract: In this presentation the stable and optimal operation of the adaptive line enhancer (ALE) is considered. By improving the estimate of the steady-state mean square error (MSE), a tighter stability constraint is obtained, as well as more accurate expressions for the SNR gain attained by the ALE when filtering sinusoidal signals in white noise. Since the LMS algorithm used for adapting the ALE weights aims at minimizing the MSE, and not at maximizing the output SNR, the proper choice of the algorithm's parameters for maximizing the SNR gain is considered. In particular, it is shown that for a given step-size parameter µ (which satisfies the stability constraint) there exists an optimal number of weights which maximizes the SNR gain. Computer simulations verify the analytical results.
TL;DR: In this paper, a general formula for the filter function of maximally flat f.i.r. digital filters using modified Krawtchouk polynomials is presented.
Abstract: A general formula for the filter function of maximally flat f.i.r. digital filters using modified Krawtchouk polynomials is presented. This formula permits a direct calculation of the filter weights. It is especially advantageous for either high or low passband/stopband width ratios.