Showing papers on "Adaptive filter published in 1983"

A fast sequential algorithm for least-squares filtering and prediction

Abstract: A new computationally efficient algorithm for sequential least-squares (LS) estimation is presented in this paper. This fast a posteriori error sequential technique (FAEST) requires 5p MADPR (multiplications and divisions per recursion) for AR modeling and 7p MADPR for LS FIR filtering, where p is the number of estimated parameters. In contrast the well-known fast Kalman algorithm requires 8p MADPR for AR modeling and 10p MADPR for FIR filtering. The increased computational speed of the introduced algorithm stems from an alternative definition of the so-called Kalman gain vector, which takes better advantage of the relationships between forward and backward linear prediction.

Discrete coefficient FIR digital filter design based upon an LMS criteria

Abstract: An efficient method optimizing (in the least square response error sense) the remaining unquantized coefficients of a FIR linear phase digital filter when one or more of the filter coefficients takes on discrete values is introduced. By incorporating this optimization method into a tree search algorithm and employing a suitable branching policy, an efficient algorithm for the design of high-order discrete coefficient FIR filters is produced. This approach can also be used to design FIR filters on a minimax basis. The minimax criterion is approximated by adjusting the least squares weighting. Results show that the least square criteria is capable of designing filters of order well beyond other approaches by a factor of three for the same computer time. The discrete coefficient spaces discussed include the evenly distributed finite wordlength space as well as the nonuniformly distributed powers-of-two space.

ECG Enhancement by Adaptive Cancellation of Electrosurgical Interference

Abstract: A technique utilizing a combination of adaptive noise canceling and conventional signal processing is developed to enhance electrocardiographic monitoring in the operating room by reducing the noise interference that is created by an electrosurgical instrument. Significant amounts of interference are eliminated by radio frequency shielding, passive and active low-pass filtering, and optical isolation. A digital adaptive canceler using the least mean-square algorithm of Widrow and Hoff is used to reduce the remainder of the interference, yielding an improvement in signal-to-noise ratio of approximately 110 dB. Clear electrocardiograms have been obtained with electrocautery equipment in operation.

A unified approach to time- and frequency-domain realization of FIR adaptive digital filters

Abstract: Specific implementations of the finite impulse response (FIR) block adaptive filter in the frequency domain are presented and some of their important properties are discussed. The time-domain block adaptive filter implemented in the frequency domain is shown to be equivalent to the frequency-domain adaptive filter (derived in the frequency domain), provided data sectioning is done properly. All of the known time- and frequency-domain adaptive filters [1]-[12], [16]-[18] are contained in the set of possible block adaptive filter structures. Thus, the block adaptive filter is generic and its formulation unifies the current theory of time- and frequency-domain FIR adaptive filter structures. A detailed analysis of overlap-save and overlap-add implementations shows that the former is to be preferred for adaptive applications because it requires less computation, a fact that is not true for fixed coefficient filters.

Mel Log Spectrum Approximation (MLSA) filter for speech synthesis

Abstract: The spectral envelope of speech can be represented efficiently by the log magnitude spectrum on the nonlinear frequency scale, which is close to mel scale (called mel-log spectrum). the mel cepstrum defined by its Fourier coefficients is also considered to have a suitable property as the parameter to represent the spectral envelope. So far, no satisfactory filter has been reported for the synthesis approximating the mel-log spectrum. This paper presents a method of constructing the mel-log spectrum approximation (MLSA) filter, which has a relatively simple structure and a low coefficient sensitivity, together with a design example of MLSA filter for speech synthesis. the transfer function of MLSA filter is represented by Pade approximation, which approximates the exponential of the transfer function of the filter (so-called basic filter). Since the transfer function of the basic filter is represented by a polynomial with the transfer function of the first-order all-pass filter as the variable, it is necessary in the realization of the filter to delete from the feedback loop the path without a delay. By the construction method of MLSA filter shown in this paper, the path without delay can easily be deleted from the feedback loop in the MLSA filter. the obtained MLSA filter is of relatively simple structure and has low coefficient sensitivity. the quantization characteristics of the coefficient are also satisfactory.

A new approach to FIR digital filters with fewer multipliers and reduced sensitivity

Abstract: A new approach to the design of efficient finite impulse response (FIR) digital filters is presented. The essence of the proposed method is to decompose the design problem into two parts: the realization of an efficient prefilter and the design of the corresponding amplitude equalizer. It is shown that this method can provide benefits in three areas: reduced computational complexity, reduced sensitivity to coefficient quantization, and reduced roundoff noise.

Dynamic ship positioning using a self-tuning Kalman filter

Abstract: A novel adaptive filtering technique is described for a class of systems with unknown disturbances. The estimator includes both a self-tuning filter and a Kalman filter. The state estimates are employed in a closed-loop feedback control scheme which is designed via the usual linear quadratic approach. The approach was developed for application to the dynamic ship positioning control problem and has the advantage that existing nonadaptive Kalman filtering systems may be easily modified to include the self-tuning feature.

Approximation of 2-D separable in denominator filters

Abstract: After introducing a two-dimensional (2-D) model for the class of causal, recursive, and separable in denominator (CRSD) filters, a technique for approximating a given 2-D filter by a CRSD filter is presented. Also, a technique for 2-D CRSD model reduction is considered. Both the stability and minimality properties of the approximate model are explored. Some examples are also given to illustrate the proposed technique.

Second-order convergence analysis of stochastic adaptive linear filtering

Abstract: The convergence of an adaptive filtering vector is studied, when it is governed by the mean-square-error gradient algorithm with constant step size. We consider the mean-square deviation between the optimal filter and the actual one during the steady state. This quantity is known to be essentially proportional to the step size of the algorithm. However, previous analyses were either heuristic, or based upon the assumption that successive observations were independent, which is far from being realistic. Actually, in most applications, two successive observation vectors share a large number of components and thus they are strongly correlated. In this work, we deal with the case of correlated observations and prove that the mean-square deviation is actually of the same order (or less) than the step size of the algorithm. This result is proved without any boundedness or barrier assumption for the algorithm, as it has been done previously in the literature to ensure the nondivergence. Our assumptions are reduced to the finite strong-memory assumption and the finite-moments assumption for the observation. They are satisfied in a very wide class of practical applications.

On the effects of varying filter bank parameters on isolated word recognition

Abstract: The vast majority of commercially available isolated word recognizers use a filter bank analysis as the front end processing for recognition. It is not well understood how the parameters of different filter banks (e.g., number of filters, types of filters, filter spacing, etc.) affect recognizer performance. In this paper we present results of performance evaluation of several types of filter bank analyzers in a speaker trained isolated word recognition test using dialed-up telephone line recordings. We have studied both DFT (discrete Fourier transform) and direct form implementations of the filter banks. We have also considered uniform and nonuniform filter spacings. The results indicate that the best performance (highest word accuracy) is obtained by both a 15-channel uniform filter bank and a 13-channel nonuniform filter bank (with channels spacing along a critical band scale). The performance of a 7-channel critical band filter bank is almost as good as that of the two best filter banks. In comparison to a conventional linear predictive coding (LPC) word recognizer, the performance of the best filter bank recognizers was, on average, several percent worse than that of an eighth-order LPC-based recognizer. A discussion as to why some filter banks performed better than others, and why the LPC-based system did the best, is given in this paper.

Two-sided linear filter identification

Abstract: It is argued that there are a number of situations in life science where it is desirable to attempt a two-sided linear filter identification. A simple method is presented for the determination of a nonparametric, two-sided linear filter from system input and output data. The time-domain filter is determined from a matrix equation involving the input autocorrelation function and the two-sided cross-correlation function. The resulting filter minimises the sum of squared differences between the actual and predicted outputs.

Convergence behavior of the constant modulus algorithm

Abstract: An adaptive filter algorithm has been developed and introduced [1] for use with constant envelope waveforms, e.g., FM communication signals. It has proven capable of suppressing additive interferers as well as equalization, without the need for a priori statistical information. In this paper, aspects of dynamic convergence behavior are discussed, with conclusions supported by simulation.

Rejection of Pulsed CW Interference in PN Spread-Spectrum Systems Using Complex Adaptive Filters

Abstract: In this paper, the performance of direct sequence QPSK spread-spectrum systems using complex adaptive filters in the presence of pulsed CW interference is analyzed. Both adaptive prediction error filters and adaptive transversal filters with two-sided taps are considered. It is shown that the time constant of the tap weight adaptation in the interference off-interval is usually much greater than the time constant in the on-interval, and that this is beneficial for the system since it results in retaining the rejection property of the filter. Under steady-state conditions, the tap weights are calculated. Analytical expressions for the signal-to-noise ratio improvement under the least favorable interference condition are given. It is shown that the performance of the two-sided transversal filter is better than that of the prediction error filter.

Detecting and estimating parameter jumps using ladder algorithms and likelihood ratio tests

Abstract: The problem of recursive identification of autoregressive processes which are subject to parameter jumps of unknown magnitude occurring at unknown times is addressed. A sequential procedure for tracking the parameters, detecting the parameter jumps and estimating the points of change is presented which is based on generalized likelihood ratio (GLR) techniques and application of two adaptive ladder filters: the unnormalized growing memory and sliding memory least squares covariance ladder algorithms. From the prediction error energies which are available from these algorithms, the relevant GLR statistics for detection and location of the parameter jumps is computed and after each jump detection the growing memory ladder algorithm is reinitialized by means of the sliding memory filter estimates.

Voice messaging system with pitch tracking based on adaptively filtered LPC residual signal

Abstract: A voice messaging system, wherein linear predictive coding (LPC) parameters, pitch, and preferably other excitation information is derived from a human voice input, encoded, and transmitted and/or stored, to be called up later to provide a speech output which is nearly identical to the original speech input. The invention features adaptive filtering of the residual signal. The residual signal derived from LPC estimation is adaptively filtered, and then is used as the input to a conventional pitch estimation procedure. The adaptive filtering step uses the first reflection coefficient (k1) to realize a simple filter (e.g., A(z)=(1-k1 z-1)-1. This filter removes high frequency noise from the residual signal during voiced periods, but does not remove the high frequency energy which contains important information during the unvoiced periods of speech. Preferably the above preprocessing technique is also combined with a postprocessing technique, wherein dynamic programming is used to optimally track pitch and voicing information through successive frames.

Adaptive array processing. A tutorial

Abstract: An array is defined as a set of sensors, for example antennas or acoustic transducers. The outputs from these sensors can be processed in an adaptive manner to respond to an unknown interference environment in an attempt to optimise the performance of the system in some defined manner. The paper is intended as an introduction to, and to some extent a survey of, some of the methods used in adaptive array processing. It has, however, no pretence to be comprehensive. Some of the topics covered include the use of constraints, gradient techniques, including the stochastic steepest-descent algorithm, properties of the covariance matrix, the power inversion algorithm and the use of weight purturbation techniques.

Adaptive enhancement of finite bandwidth signals in white Gaussian noise

Abstract: The steady-state behavior of the adaptive line enhancer (ALE) is analyzed for stationary inputs consisting of finite bandwidth signals embedded in a white Gaussian noise (WGN) background. Analytic expressions for the weights and output of the LMS adaptive filter are derived as functions of input signal bandwidth and SNR, as well as ALE length and bulk delay. The steady-state gain in broad-band SNR from input to output is derived as a function of these same four variables. For fixed ALE parameters and input SNR, it is shown that this gain increases as the input signal becomes narrower and approaches the sinusoidal limit. It is emphasized that because the correlation time of finite bandwidth signals is limited, excessively large values of the ALE bulk delay parameter result in diminished gain. Furthermore, there is an optimal filter length, whose value depends upon signal bandwidth and SNR, for which the broad-band gain is maximized. These results demonstrate the importance of including the effects of algorithm noise in analyzing the performance of real-time adaptive processors.

Efficient sampling rate alteration using recursive (IIR) digital filters

Abstract: A general scheme for changing sampling rates is developed, and a method of designing recursive (IIR) filters for use in this general scheme is presented. If phase response is ignored, then this scheme provides a considerable savings in computations over nonrecursive (FIR) schemes. The use of recursive filters with approximately linear phase also led to a performance better than that using nonrecursive filters. The quantization effects in the new scheme are minimal.

Singular-value decomposition approach to time series modelling

Abstract: In various signal processing applications, as exemplified by spectral analysis, deconvolution and adaptive filtering, the parameters of a linear recursive model are to be selected so that the model is `most' representative of a given set of time series observations For many of these applications, the parameters are known to satisfy a theoretical recursive relationship involving the time series' autocorrelation lags Conceptually, one may then use this recursive relationship, with appropriate autocorrelation lag estimates substituted, to effect estimates for the operator's parameters A procedure for carrying out this parameter estimation is given which makes use of the singular-value decomposition (SVD) of an extended-order autocorrelation matrix associated with the given time series Unlike other SVD modelling methods, however, the approach developed does not require a full-order SVD determination Only a small subset of the matrix's singular values and associated characteristic vectors need be computed This feature can significantly alleviate an otherwise overwhelming computational burden that is necessitated when generating a full-order SVD Furthermore, the modelling performance of this new method has been found empirically to excel that of a near maximum-likelihood SVD method as well as several other more traditional modelling methods

IIR algorithms for adaptive line enhancement

Abstract: In this paper we introduce a simple IIR structure for the adaptive line enhancer. Two algorithms based on gradient-search techniques are presented for adapting the structure. Results from experiments which utilized real data as well as computer simulations are provided.

Thinning digital filters: A piecewise-exponential approximation approach

Abstract: An algorithm is presented for designing finite impulse response (FIR) recursive digital filters that require few multiplies to produce good frequency response, The process of reducing the number of multiplies needed to implement a digital filter is called thinning. This thinning algorithm uses dynamic programming techniques to find the best least-squares piecewise-exponential approximation to a desired impulse response of length P. Because these filters are implemented recursively, the number of arithmetic operations is independent of the model filter length P and is dependent only on the number of pieces or segments S used in the approximation ( S \ll P ). Examples of thinned narrow-band, broad-band, lowpass, and bandpass filters are given. Several of these thinned filters require fewer than one-third the number of multiplications required for the corresponding model filter, while still retaining desirable frequency response characteristics. The effects of coefficient quantization and finite precision arithmetic are also discussed.

A digital adaptive filter using a memory-accumulator architecture: Theory and realization

Abstract: This paper presents a novel approach to the construction of an adaptive filter making use of the so-called "distributed arithmetic" filter architecture originally suggested by Peled and Liu [7] for the realization of fixed response digital frequency filters. The technique uses only the operations of memory access, addition, and scaling, without the need for digital multiplication. Since multiplication is often quoted as the major bottleneck in digital signal processing structures, the system derives considerable advantage by the exclusion of this operation. The paper presents the derivation of a new adaptive algorithm based on this particular hardware structure, although no rigorous theoretical proof of the algorithm convergence properties is given. Computer simulations are included to demonstrate some of the basic operational characteristics of the structure. Finally, results from a hardware prototype, constructed using standard TTL integrated circuits, are presented. This approach differs from contemporary ideas which depend on the use of digital multipliers in either custom VLSI designs or using standard signal processing chips. It offers high-bandwidth operation at low cost using devices which are already in great demand by the computer market. Alternatively, the algorithm is ideal for implementation as a microprocessor-based system which could operate on real-time voice-bandwidth signals with a minimum of peripheral interface circuitry.

Adaptive tracking of multiple hot-spot target IR images

Abstract: In the recent past, the capability of tracking dynamic targets from forward-looking infrared (FLIR) measurements has been improved substantially, by replacing standard correlation trackers with adaptive extended Kalman filters. This research investigates a tracker able to handle "multiple hot-spot" targets, in which digital (or optical) signal processing is employed on the FLIR data to identify the underlying target shape. This identified shape is then used in the measurement model portion of the filter as it estimates target offset from the center of the field-of-view. In this algorithm, an extended Kalman filter processes the raw intensity measurements from the FLIR to produce target estimates. An alternative algorithm uses a linear Kalman filter to process the position indications of an enhanced correlator in order to generate tracking estimates; the enhancement is accomplished not only by thresholding to eliminate poor correlation information, but also by incorporating the dynamics information from the Kalman filter and the on-line identification of the target shape as a template instead of merely using previous frames of data. The performance capabilities of these two algorithms are evaluated under various tracking environment conditions and for a range of choices of design parameters.

The Asymptotic Optimal Frequency Domain Filter for Edge Detection

Abstract: In an earlier paper by Shanmugam, Dickey, and Green, an edge detection filter was derived which maximized the energy within a specified interval about an edge feature. The initial expression of this filter involved a prolate spheroidal wave function. However, a careful analysis of the application of an asymptotic approximation to this function uncovered a major dimensional error. The corrected derivation of the asymptotic optimal filter forms the subject of this paper. To verify the results, the filter found is compared to a similar filter developed independently by Marr and Hildreth.

Roundoff noise in error-feedback state-space filters

Abstract: Error-feedback is an effective technique for reducing multiplier wordlengths in fixed-point recursive digital filter implementations. In this paper, a new roundoff noise formula for error-feedback state-space filters is derived and used in optimizations.

Fast, fixed-order, least-squares algorithms for adaptive filtering

Abstract: Fast, fixed-order, exact-least-squares algorithms for tapped-delay-line adaptive-filtering applications are presented in this paper. These new recursive algorithms require fewer operations per iteration and exhibit better numerical properties than the so-called Fast-Kalman algorithm of Ljung and Falconer [1978] and the unnormalized, least-squares, joint-process-lattice algorithms of Morf and Lee [1978]. In comparison with the currently used stochastic-gradient or LMS adaptive algorithm of Widrow and Hoff, the new, fixed-order, least-squares algorithms yield substantial improvements in transient behavior at a modest increase in computational complexity. Additionally, over a wide range of practical applications, the new algorithms demonstrate numerical properties comparable to those of the normalized lattice introduced by Lee, Morf, and Friedlander [1981], but at a considerable reduction in complexity.

Eigenfilter approaches to adaptive array processing

Abstract: High-resolution methods of spectral estimation have recently found application in the processing of data received by spatially distributed arrays of sensors. These techniques have been used for estimation of the directional power illuminating the array and have proved useful as a first step in the analysis and classification of possible targets. Several desirable qualities are offered by these spatial spectral analysis techniques: high resolution for short arrays, low sidelobe levels, ability to work with arbitrary array geometries, and tolerance to correlated (multipath) targets being but a few. Spectral estimators based on certain eigenvectors of the data covariance matrix have these properties. The paper discusses the use of filters based on these eigenvectors (called eigenfilters) in adaptive array processing, and two different estimation algorithms are compared. The first algorithm involves a simple gradient descent approach, while the second utilises a decomposition of the array data by a spatial filter which leads to a triangular structured lattice filter. Comparisions are made between these algorithms and with other conventional high-resolution spectral estimators.

Design of circularly symmetric two-dimensional FIR digital filters employing transformations with variable parameters

Abstract: A modification of the trasformation method used for the design of two-dimensional (2-D) circularly symmetric finite impulse response (FIR) digital filters from one-dimensional (1-D) filters is described. This modification entails the embedding of variable parameters in the different transformations applied to the different factors of the 1-D reference function. This new method results in the design of 2-D FIR filters whose frequency response characteristics meet the cutoff boundary specifications more closely than the transformations without the modification. This method is quite useful for the design of 2-D FIR filters with multiple cutoff boundaries such as bandpass filters.

A fast convergence frequency domain adaptive filter

Abstract: This correspondence presents a new fast convergence algorithm for frequency domain adaptive filter and its applicability to acoustic noise cancellation in speech signals.