Showing papers on "Kernel adaptive filter published in 1980"

Fast implementations of LMS adaptive filters

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.

An analysis of multiple correlation cancellation loops with a filter in the auxiliary path

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.

Minimum Peak Range Sidelobe Filters for Binary Phase-Coded Waveforms

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.

On a realization and related algorithm for adaptive prediction

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.

An Adaptive Two-Dimensional Kalman Tracking Filter

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.

Adaptive filter theory and applications

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

Learning characteristics of adaptive lattice filtering algorithms

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.

Adaptive Filter Structures for Deconvolution of Seismic Signals

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.

A unified approach to optimal recursive digital filter design

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.

Image processing apparatus including a partitioned low pass channel

Abstract: An electronic image processing system, providing image enhancement and noise suppression, processes signals representing an array of picture elements, or pels. The system is of the kind providing a feature-enhanced output by the addition of outputs of a high-pass filter acting as an image-feature detector and a complementary low-pass filter. The low-pass filter, which also acts as an image-feature detector, includes a bandpass filter and a further low-pass filter. The latter filter (122) includes a prefilter (130 and FIG. 22) and a sub-sampling filter (132) based on a set of weighting patterns in the form of sparse matrices (FIG. 23). The bandpass filter (120) includes a similar prefilter (128) and a sub-sampling filter (134) based on a set of weighting patterns in the form of sparse matrices (FIGS. 26 and 27) that act as detectors of selected image features.

Two-stage adaptive noise cancellation for intermittent-signal applications (Corresp.)

Abstract: A novel two-stage adaptive signal extractor for intermittent signal applications is presented. If the presence and absence of the signal can be detected, the first stage will adapt only while the signal is absent and thereby effect a reduction in noise, whereas the second stage will adapt only when the signal is present and thereby effect a reduction in the distortion caused by the gust stage. Bounds on performance are derived, and performance improvement relative to a conventional one-stage adaptive noise canceller is assessed.

Method of and device for testing the quality of digital speech-transmission equipment

Abstract: A device for analyzing the quality of digital speech-transmission equipment, specifically a speech coder, comprises generators of white-noise signals, sinusoidal signals, frequency-shaped signals and artificial speech-like signals connected to the coder and to an adaptive transversal filter in parallel therewith. The filter and the coder feed output signals to a subtractor which produces an error or noise signal by deducting digital samples of the filter output signal from corresponding samples of the coder output signal. The error signal is fed back to the filter during a first testing phase to control the periodic modification of weighting coefficients computed by a multiplicity of updating cells in the filter for multiplicative combination with incoming samples of the test signal. The first phase ends when the coefficients converge to fixed values representative of the linear characteristics of the coder and the error signal assumes a substantially constant near-zero level. In a second testing phase the error signal from the subtractor and the corrected output signal of the filter are fed to a quality analyzer which calculates one or more parameters from a set comprising a total signal-to-noise ratio, a simple segmental signal-to-noise ratio and a frequency-weighted segmental signal-to-noise ratio, the parameters being linearly combined to produce an integer between zero and ten indicative of the transmission quality of the coder.

The recursive adaptive LMS filter--A line enhancer application and analytical model for the mean weight behavior

Abstract: A mathematical model is presented for the recursive adaptive filter (RAF) configured as an adaptive line enhancer (ALE) in the frequency domain. The inputs for the model are Markovian and the number of recursive taps is selected to equal the order of the Markov process. Thus, the RAF structure is sufficient to realize the Wiener filter. Assuming that the expectations of all filter-data interactions factor, a system of four deterministic equations for the mean weights is derived. In steady state, the mean weights converge to the Wiener filter, and hence minimize the mean-square error. Excellent agreement between this analysis and stochastic simulations support the expectation-splitting assumptions.

Apparatus for damping operator induced oscillations of a controlled system

Abstract: Apparatus for damping operator induced oscillations of a controlled system responding to an operator controlled signal (DEP) utilizing a lag-lead filter (14) for frequency and amplitude estimation of the control input, and a rectification and smoothing filter (16) for producing a signal proportional to the absolute value of the frequency and amplitude estimate for use in suppression of the control system output signal (DEC). In one embodiment, this is accomplished by computing a correction signal in a correction generating section (18). In a second embodiment, a second rectification and smoothing filter (21) produces a signal proportional to the absolute value of the controlled input signal. A ratio of the outputs of the first and second rectification and smoothing filters is then used in a generator (24) to generate a gain factor k q for the control system to reduce the gain of the output signal of the control system, thereby to provide a damped control output signal without rate limiting the controlled element.

Automatic equalizer for synthesizing recursive filters

Abstract: A recursive automatic equalizer is described for implementing the telephone equalization function at a line circuit which may be multiplexed between a plurality of telephone subscriber sets. A recursive digital filter structure having programmable coefficients minimizes the error between the equalizer input and a reference signal. The recursive filter transfer function is variable via feedback coefficient update, with respect to its input and the reference signal. The recursive filter coefficients are adaptively changed to rapidly converge to a final value based upon a mean square error algorithm. The desired filter transfer function can be achieved with a low number of coefficients, for example, five, rather than the heretofore high number of coefficients required in non-recursive filter structures.

Block adaptive filtering

Abstract: Block filtering implies the calculation of a block or finite set of filter outputs from a block of input values. A procedure for doing this with adaptive filters is developed along with analyses of convergence properties and computational complexity. It is shown that the block approach can offer advantages in convergence speed, accuracy and computational complexity over conventional adaptive filtering techniques under the proper circumstances.

A stable family of adaptive IIR filters

Abstract: This paper unifies a rapidly growing area of inquiry: adaptive infinite impulse response (IIR) filter development and evaluation. A class of stably convergent adaptive IIR filters has evolved from a proven adaptive IIR filter with an auto-regressive, moving-average (ARMA) structure. The convergence proof was originally based on a hyperstability evaluation of an identification format. This paper relates this format to those arising from other applicable nonlinear stability theory results. These interrelationships allow statement of a growing list of adaptive IIR filter properties. The alternate search/minimization technique of adaptive IIR filler development is also derived and contrasted. This results in a proposal of a general adaptive filter algorithm.

Convergence properties of an adaptive digital lattice filter

Abstract: Convergence properties of a continuously adaptive digital lattice filter used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean value and the output mean square error are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curves using this model are compared with simulation results. Results obtained for a two stage lattice are then compared with the two-stage 1ms transversal filter algorithm, demonstrating that it is possible but unlikely for the transversal filter to converge faster than the analogous lattice filter.

A Wiener filter approach to coherence estimation

Abstract: This paper presents a new method of estimating the magnitude-squared-coherence (MSC) between two random processes x and y . An unrealizable Wiener filter transfer function is first computed in the x channel, then another one in the y channel. The product of these transfer functions then gives the MSC. The unrealizable Wiener filter is approximated by a finite impulse filter whose coefficients can be computed from any standard parameter estimation algorithm. Theoretical upper bounds on the variances of the MSC estimates, together with experimental results, are given.

Identification of unknown parameters in linear discrete-time systems by a modified extended Kalman filter

Abstract: This paper treats an identification technique for discrete-time linear systems whim noisy measurements are taken. The technique is based on the extended Kalman filter and the model reference adaptive approach. Firstly, the extended Kalman filter derived by augmenting unknown parameters as the state variables is modified by neglecting the information between the states and unknown parameters ; and secondly the stability of the modified filter is compensated by the idea of the model reference adaptive approach. Lastly, the convergence of the obtained estimates for unknovm parameters to the exact values is proved. A numerical example shows the effectiveness of the proposed method.

A comparison of algorithms for the calculation of adaptive lattice filters

Abstract: This paper presents a comparison of four different algorithms for calculating the reflection coefficients of a lattice-structure prediction-error filter. This filter structure is gaining increasing use in the fields of adaptive filtering and linear prediction analysis. Comparison is made of the harmonic-mean (also known as Burg's algorithm), the geometric-mean, the forward-and-backward, and the forward/backward-minimum algorithms. Also compared are two methods of implementing the algorithms as recursive, continuously adaptive calculations. Results using simulated radar returns as the test signal are presented.

Recursive lattice filters - A brief overview

Abstract: Recently lattice filters structures have been employed in numerous adaptive filtering applications such as noise cancelling; speech processing; and data equalization. In this paper we will be concerned with the algorithms that have been proposed to update the lattice filter coefficients. These algorithms typically fall into one of two classes, those based on stochastic (gradient) formulations and those founded on a least squares criterion. The latter are more complex; however, they provide for a faster response to sudden changes in the input data (e.g., a rapid initial convergence). It is the purpose of this paper to provide a brief exposition of the algorithms and to point out their various parallels. In particular, it is hoped that the simpler structure of the stochastic algorithms will shed some light on the more complex least squares procedures.

Use of a programmable filter for inverse filtering

Abstract: A real-time deconvolution or inverse filter, operating at signal frequencies up to 5 MHz, is reported. The programmable digital filter is controlled by a computer which calculates the Wiener-filter solution using f.f.t. techniques. Deconvolved signals can be clearly discriminated after passing through a distorting medium.

2-D causal recursive digital filter modeling implementation

Abstract: A technique is proposed to find the causal recursive filter model that realizes a given impulse response support. This is shown to be an essential step in any design problem. Although, the local state space (LSS) is used throughout the paper to represent the filter model, all results apply as well to the transfer function representation. Several aspects of the implementation problem which are dependent on the choice of the filter model are also discussed. These are the recursion sequence, the implementation storage and the possible parallelism in calculating the output samples.

New charge transfer recursive filter

Abstract: A new fully integrated recursive charge transfer filter is described using the concept of passive recirculation of charges. This is a novel solution for voice channel or data filters in digital communications.

Three-Dimensional Shuman Filter.

Abstract: Equations for the three-dimensional Shuman filter and its response function are presented. The filter heavily dampens even fairly long waves, but this can be alleviated somewhat by a second amplifying pass (tandem filter). Due to the number of points (27) needed to filter the data, irregular boundaries and missing data can be a problem. The effects of one possible solution (reverting to three one-dimensional filters) is shown. Under some instances it is preferable to make only one pass to prevent propagation of boundary discontinuities.

A monolithic c.c.d. programmable transversal filter for analogue signal processing

Abstract: This paper describes the operational features and performance of a fully-integrated programmable transversal filter (p.t.f.), using c.c.d./m.o.s.t. technology. The choice of filter architecture for a prototype realization is discussed with particular reference to a novel multiplier array implementation using a single, time-multiplexed m.o.s. transistor. The performance characteristics of a prototype, 64-point filter design based on this approach are detailed with reference to frequency- and matched- filtering. Techniques for optimizing the performance of this analogue filter structure under microprocessor control are suggested, through the iterative adaption of the filter impulse response, and equivalent results are given to show the improvement gained. An alternative technique for improving the filter characteristics which enables it to optimize the processing of signals under certain conditions has also been demonstrated. This adaptive filter configuration is based on the linear Widrow least-mean-square (W.l.m.s.) algorithm, and has been realized using the p.t.f. with minimal additional circuitry, without the requirement for a microprocessor.A general signal-processing module of 256-points using four cascaded filters is described; and results are presented when it is used in a sonar, matched-filtering experiment. Also a 64-point adaptive filter based on a prototype p.t.f. is described and its application to inverse filtering and self-tuning filtering is demonstrated.Finally, the potential of this miniature integrated filter for sonar-type applications is reviewed against new developments. In particular, a 256-point monolithic p.t.f.currently in development, and the concept of a dedicated adaptive filter in single chip form.

Adaptive Background Suppression For Real-Time Signal Processing

Abstract: When using an adaptive filter for real-time signal processing, the filter coefficients must be modified in real time and fast computation methods for determining optimal filter coefficients are essential. The optimal coefficients for signal detection and background suppression depend upon the statistics of the background noise and the characteristics of the signal pulse. A novel approach for calculating optimal filter coefficients has been devised which makes use of symmetries in the coefficients (derived from symmetries in the noise statistics and the signal) to reduce computation requirements significantly. For example, with a five-by-five two-dimensional spatial filter there are 25 coefficients which must be determined, and the conventional approach requires over 5000 multiplications and 5000 additions. When symmetries exist, there are only 6 distinct values for the 25 coefficients, which reduces the required calculations to 75 multiplications and 75 additions. Detailed examples of temporal filtering, spatial filtering, and multispectral filtering illustrate the efficacy of the procedure in practical situations.