Showing papers on "Kernel adaptive filter published in 1987"

Theory and design of adaptive filters

Abstract: THE NEED FOR ADAPTIVE FILTERING: Removal of Power Line Hum From Medical Instruments Adaptive Differential Pulse Code Modulation Equalization of Troposcatter Communication Signals Generality and Commonality BASIC PRINCIPLES OF ADAPTIVE FILTERING: The FIR Linear Combiner Number Guessing Games Single-Integer Guessing Games Multiple- Integer Guessing Games Multiple Real Numbers Guessing Games Adaptive Filter Algorithm Interpretation AN ANALYTICAL FRAMEWORK FOR DEVELOPNG ADAPTIVE ALGORITHMS: Background and Direction The Least Squares Problem Basic Formulation Reduction to the Normal Equations Direct Solution to the Optimal Vector The Meaning of P and P Examples Matching a Pure Delay Matching a Rotated Phasor Two Solution Technique Direct Inversion Iterative Approximation Computational Comparisons Consolidation The Least Mean Square Problem Formulation A Brief Review of Stochastic Process Ensemble Averages An Example Stationarity The Concept of White Noise.

Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images

Abstract: A novel adaptive median filter is proposed. It allows the simultaneous removal of a combination of signal-dependent and additive random noise in addition to mixed impulse noise in images, processed in a single filtering pass. The adaptation algorithm is based on the local signal-to-noise ratio. An extension of the class of nonlinear mean filters to adaptive filters is considered. The performance of the adaptive median filter is compared to the commonly used median filter and the nonlinear mean filter.

Reduced-order estimation Part 1. Filtering

Abstract: This paper presents a method for designing an ‘optimum’ unbiased reduced-order filter. For the proposed approach to work, the order of the filter must be greater than a certain minimum determined by the number of independent observations of the system available. The filler is much like a Luenberger observer for the state to be estimated, but with parameters optimized with respect to the noises in the system. A reduced-order innovation process is proposed that has properties similar to those of the full-order innovation process when the reduced filter is optimized. The approach offers the possibility of significant reduction in real-time computational requirements compared with the full-order filter, though at the cost of some loss of performance. The algorithm for the reduced-order filter is simple to implement— quite similar to that of the Kalman filter. An example is presented to compare the performance of the proposed method with the full-order Kalman filter.

A second-order adaptive Volterra filter with rapid convergence

Abstract: An algorithm for updating the linear and quadratic weights of a second-order Volterra filter (SVF) is proposed. This algorithm uses a fast Kalman filter algorithm to calculate the Kalman gain vector used in updating the linear and quadratic weights of the SVF. The convergence of the algorithm for the quadratic weights is established. A simulation is then performed in which a fast Kalman filter implementation of an SVF is compared to an LMS implementation in a system identification problem. The fast Kalman filter implementation is shown to converge to the unknown system parameters considerably faster than the LMS implementation.

Concurrent cellular VLSI adaptive filter architectures

Abstract: Previous approaches to high-sampling-rate adaptive filter implementations have been based on word-level pipelined word-parallel (or "block") realizations. In this paper, we show that adaptive filters can be implemented in an area-efficient manner by first using pipelining to the maximum possible extent, and then using block processing in combination with pipelining if further increase in sampling rate is needed. We show that, with the use of a decomposition technique, high-speed realizations can be achieved using pipelining with a logarithmic increase in hardware (the block realizations require a linear increase in hardware). We derive pipelined word-parallel realizations of high-sampling-rate adaptive lattice filters using the techniques of look-ahead computation decomposed state update implementation, and incremental output computation. These three techniques combined make it possible to achieve asymptotically optimal complexity realizations (i.e., the same complexity asymptotically as nonrecursive systems) of high-speed adaptive lattice filters (in both bit-serial and bit-parallel methodologies) and provide a "system solution" to high-speed adaptive filtering. The adaptive lattice filter structures are ideal for high-sampling-rate implementations, since the error residuals of a particular stage are adapted order-recursively based on those of the previous stage, and the coefficient update recursion inside each stage is linear in nature. An example of a normalized stochastic gradient adaptive lattice filter is presented, and its complexity, latency, and implementation methodology tradeoffs are studied.

Convergence of the RLS and LMS adaptive filters

Abstract: The paper presents new convergence results for two adaptive filters: the RLS and LMS algorithms. Convergence of the exact RLS algorithm is studied when the forgetting factor \lambda is constant, which enables the adaptive filter to track time variations of the optimal filter. It is shown that, in the steady state, the squared deviation of the adaptive filter from the optimal one admits, with probability 1- \epsilon ( \epsilon arbitrarily small), an upper bound that is proportional to the (infinitesimal) quantity \mu = 1 - {\lambda} . This result agrees with the algorithm's practical behavior. The bound increases with the correlation degree of the filter inputs. This paper also provides an almost sure convergence result concerned with the LMS algorithm with decreasing step-size (infinite memory), used only when the optimal filter is asymptotically time-invariant, although the input statistics may be time-varying.

Simplified analysis of the LMS adaptive filter using a transfer function approximation

Abstract: A simple approximation to the behavior of the LMS adaptive filter as a discrete transfer function is developed. This representation is a valid description for both deterministic inputs and for the expected results with random inputs (including correlated inputs). The results are shown to be exact for some classes of input including periodic signals. One result of this analysis is the demonstration that the LMS filter can produce results which are biased from the least-squares solution under the combined conditions of a nonzero mean primary and a correlated reference input.

System modeling using an adaptive delay filter

Abstract: In this paper, we present a special technique for modeling an unknown system. This technique requires a type of adaptive filter called an Adaptive Delay Filter [1]-[3]. This filter structure includes variable delay taps in addition to variable gains. The Adaptive Delay Filter is especially applicable to system modeling problems in which the system to be modeled has a sparse impulse response [4]-[7]. Using the standard adaptive filter to model a sparse system could require a very large filter [8], while an Adaptive Delay Filter could model the sparse impulse response with very few elements in the filter since the delay taps spread out to adapt to the unknown system. An analysis of the mean-squared error surface using this technique is included, along with the computer simulation results of the performance in modeling both the delay taps and the gains of an unknown system. A comparison of this technique with the conventional approach using Widrow's LMS algorithm will be addressed.

Tone signal processing device using a digital filter

Abstract: At least two sets of filter coefficients corresponding to different filter characteristics are interpolated by using a control signal for controlling tone color as a parameter of interpolation. Filter coefficients obtained by the interpolation are supplied to a digital filter to determine its filter characteristics and an input tone signal is modified in accordance with the filter characteristics thus determined. Filter characteristics of diverse variation as compared with the number of prepared filter coefficients can thereby be realized. Further, timewise change of filter characteristics can be realized by changing a parameter of interpolation with lapse of time or changing two sets of filter coefficients to be interpolated with lapse of time. Designation of filter coefficients can be made by designating coordinate data of coordinates having at least two axes. In this case, filter coefficients can be changed by changing coordinate data of at least one axis in accordance with tone color control information whereby filter characteristics can be variably controlled.

Spacecraft attitude determination using a second-order nonlinear filter

Abstract: The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

Adaptive Control for Flexible Space Structures with Measurement Noise

Abstract: This paper addresses the problem of measurement noise in the design of adaptive controllers for large space structures. It is shown by simulation that although the well known "fix" based on introducing leakage terms in the adaptive law is successful in eliminating drift in the adaptive gains, the overall control demand remains unrealistically high. In order to reduce the control demand, it is necessary to attenuate the noise by introducing filters at key positions in the adaptive configuration. However, arbitrary choice and placement of noise filters is dangerous since filter lags tend to cause instabilities in the adaptation loop. This phenomenon is verified by simulation. The main result of this paper is a general theory for introducing noise filters into the adaptive controller so as to effectively suppress measurement noise, while insuring global stability of the adaptive algorithm. This result utilizes the new concept of a "branch filter" which allows filtering of the output error without introducing phase lag into the adaptive loop. The effectiveness of the proposed methods are demonstrated by simulation on an adaptive payload articulation control for a Space Station model.

Circuitry for converting measurement signals disturbed by a disturbance function into undisturbed signals

Abstract: A filter circuitry for disturbed signals of changing frequencies The disturbed signals are supplied to a first adaptive ladder filter (3) and to a second adaptive ladder filter (5), via a high-pass filter (4) In time intervals determined by a control circuit, during which only the disturbance reaches the second adaptive ladder filter, the parameters for filtering-out the disturbing signals are determined in the second filter, then transferred, if necessary, to the first adaptive ladder filter

Estimation of impulse‐response signals through empirically derived digital matched filter smoothing

Abstract: A real‐time digital filter is described which may be most useful for optimal determination of the magnitude of impulse‐response functions found in pulsed, repetitive experiments of low duty cycle. This filter is based on a matched filter but employs an interference orthogonalization step. This results in a signal magnitude estimate which is independent of coherent interference. The filter updates the signal magnitude estimate upon each repetition of the experimental cycle. Comparisons to signal estimation using gated sampling devices are given.

Decision-directed nonlinear filter for image processing

Abstract: A complete quadratic Volterra filter working under the control of a decision algorithm is proposed for image restoration and enhancement. A particular application in the enhancement of images having reduced luminance dynamics is considered.

Median-type filters with adaptive substructures

Abstract: In this paper, adaptive filter structures suitable for filtering signals with rapidly varying characteristics are presented. In the proposed Adaptive Median Hybrid (AMH) filters, adaptive filter substructures are used to estimate the current signal value from future signal values and from past input or output signal values. The output of the overall filter is the median of the adaptive filter outputs and the input signal value. Using computer simulations, the convergence properties of the filters in stationary and nonstationary signals have been analyzed. Due to the median operation, the AMH filters adapt and preserve abrupt changes in signal statistics substantially better than conventional adaptive filters.

Suppression and detection of impulse type interference using adaptive median hybrid filters

Abstract: In this paper, we introduce a new type of nonlinear filters, the Adaptive Median Hybrid (AMH) filters, for the suppression and detection of short duration interferences. In the AMH filters, adaptive filter substructures are used to estimate the current signal value from the future and past signal values. The output of the overall filter is the median of the adaptive filter outputs and the current signal value. This kind of nonlinear filter structure is shown to adapt and preserve rapid changes in signal characteristics well. However, it filters out short duration interferences. By examining the difference between the original and filtered data, interferences can be detected. We introduce two types of AMH filters, the AMH filter with separate adaptive substructures (SAMH) and the AMH filter with coupled substructures (CAMH), which have different convergence properties and implementation. We use both synthetic and real data (speech and electroencephalogram (EEG)) to show the applicability of the proposed filters.

Method and apparatus for estimating signal components of a filter output

Abstract: An adaptive matched filter is described as a replacement for a signal replica filter of a maximum likelihood (ML) demodulator, the filter normally receiving an input data vector having signal and noise components. A symbol-justified input data vector is produced from the input data vector. This vector is then used to generate a weight vector. In a time-domain embodiment, the symbol-justified data vector is multiplied by the weight vector on a symbol-by-symbol basis to estimate the signal components of the output of a filter. In a frequency-domain implementation, multiplication is carried out on a symbol block-by-symbol block basis.

A variable-step adaptation algorithm for memory-oriented Volterra filters

Abstract: A recently proposed adaptation algorithm for digital non-linear adaptive filters based on the truncated discrete Volterra series is modified and improved in order to get a faster and more accurate convergence. Performance examples are presented.

Engineering aspects of fast least squares algorithms in transversal adaptive filters

Abstract: The introduction of Fast Least Squares (FLS) algorithms in real transversal adaptive filters implies that some modifications be made to the theoretical algorithms, that engineering parameters be introduced, and that information be provided to guide the practitioner in the selection of parameter values. These aspects are discussed in the present contribution. Implementation on a programmable signal microprocessor confirms, in broad lines, the theoretical results.

Tracking characteristics of the constrained IIR adaptive notch filter

Abstract: In this paper, we study the tracking behavior of a constrained IIR notch filter whose coefficients are estimated using a constant step size Gauss-Newton algorithm. Using weak convergence theory and the concept of prescaling, it is shown that the mean behavior of the adaptive filter coefficients can be studied by using an ordinary differential equation (ODE). Approximate and simple closed form results are derived for the tracking behavior of a second order notch filter. Computer simulations are presented to substantiate the analysis.

A new structure for adaptive linear-phase filtering

Abstract: In many applications, including time-varying modeling and adaptive noise canceling, it is desirable to adaptively perform linear-phase filtering to prevent any phase distortion in the observed data. Much attention has been devoted so far to the purely algorithmic aspect of this problem, i.e., how to design an appropriate adaptation algorithm. It is also of interest, however, to measure the extent of the structural influence, i.e., what system structure to choose to "optimize" the performance-to-cost ratio. This paper presents a different parameterization of the linear-phase filtering problem, naturally leading to a new, very efficient filter implementation. The resulting realization is then used as the system structure for an adaptive linear-phase filter. When associated with suitable stochastic gradient algorithms, this new adaptive structure is found to exhibit a tracking performance competitive with that of linear-phase lattice/ladder realizations, for a computational complexity 33 to 40 percent lower. It can therefore be used advantageously in virtually all applications of adaptive filtering. Simulations performed in the context of adaptive linear-phase modeling show good agreement with the theoretical analysis.

Adaptive stochastic filters with no strict positive real condition

Abstract: A new adaptive stochastic filter structure is introduced which avoids the strict passivity test used as a sufficient condition for convergence required by existing adaptive schemes. The proposed algorithm consists of three stages. In the first stage, an autoregressive model is fitted and the residue obtained is used as an estimate of the noise. In the second stage, an autoregressive recursive moving average model is fitted using the residual of the first stage. A modified residual is then filtered using a parameter δ and the model obtained from the second stage to generate an improved estimate of the noise. In the third stage, this improved estimate of the noise is used to obtain a better autoregressive moving average model. It is shown that the proposed algorithm will also reduce the bias in the estimated parameters. The simulation results given show that the proposed filter compares favorably to the algorithm introduced by Mayne and Clark and also Landau. This filter is then applied to the adaptive line enhancement, sinusoidal detection, and adaptive spectral estimation problems to illustrate its usefulness.

Digital adaptive filter for a high throughput digital adaptive processor

Abstract: The weights of least mean square (LMS) adaptive filter are updated with a different set of taps than are used to form the output of the adaptive filter in the adaptive processing device of the present invention. As a result of performing the multiplications and sums required for the filter operation simultaneously, an integral number of clock cycle delays appear in the narrowband and error feedback channels. The number of taps of the tapped delay line of the invention are increased, whereby the increased delay through the delay line may be used to compensate for a delay through the filter of an integral number of clock delay cycles. Instantaneous weight updating in accordance with the signal being utilized, may then be achieved at a clock rate frequency that is ten times or more greater than prior art adaptive filters.

Reducing cocktail party noise by adaptive array filtering

Abstract: This paper introduces an adaptive filtering method that rejects interfering speech from nonpreferred directions. Phase delays in interfering signals received by three receivers are used to obtain a low signal-to-noise ratio (SNR) reference input for an adaptive filter. Since adaptive filter output SNR is inversely proportional to its reference input SNR, this method works very well theoretically. To minimize the excess steady-state error in conventional schemes, a master-slave structure is introduced. Simulations with digitized speech data, tested both by comparing waveforms and by listening, showed significant improvements. Due to the computational simplicity of the method, real-time realization is feasible.

A transform domain circular convolution algorithm for adaptive filtering

Abstract: A new transform domain adaptive digital filter using the common LMS adaptation algorithm based on a highly efficient circular convolution algorithm is presented. The proposed filter has an easily implementable structure involving only real-valued arithmetic. It is shown to converge to the Wiener solution in the transform domain and has significant advantage in efficiency over the corresponding FFT-based adaptive digital filters.

Adaptive Filters for Suppressing Irregular Hostile Jamming in Direct Sequence Spread-Spectrum System

Abstract: The purpose of this paper is to design a stable and high-performance adaptive filter for suppressing irregular hostile jamming in direct sequence(DS) spread-spectrum systems A gradient-search fast converging algorithm is suggested(for convenience, named GFC) And for the case of a sudden parameter jump or incoming of an interference, the transient behaviors of the receiver using an GFC adaptive filter are investigated and compared with those of the receiver using an LMS or a Lattice adaptive filter Then the results are shown in the response graphs of the simulated receiver during the short period when the characteristic of a jammer is suddenly changed Steady state performances of those receivers are also evaluated in the sense of the excess mean square error over that of an optimum receiver for suppressing stationary interferences Since the suggested algorithm performs better than LMS or Lattice algorithm, this algorithm is principlly applicable to designing an appropriate adaptive filter for suppressing irregular hostile jamming in DS spread spectrum system

Multi-pulse coding system

Abstract: A digital speech signal sampled at a predetermined interval is stored in a memory. An LPC (Linear Prediction Coefficient) DDK is developed from the speech signal and thus developed LPC coefficient specifies coefficients of a recursive filter. The speech signal read out from the memory is backwardly supplied to the recursive filter in the reverse order to the sampling order of the speech signal. A plurality of multi-pulses are determined on the basis of the crosscorrelation coefficients (between the speech and an impulse response of the recursive filter) obtained by the recursive filter.

A cascadable adaptive FIR filter VLSI IC

Abstract: This paper describes the architecture and features of the Motorola DSP56200, an algorithm-specific cascadable digital signal processing peripheral designed to perform the computationally intensive tasks associated with FIR and adaptive FIR digital filtering applications. The DSP56200 is implemented in high performance, low power 1.5µm HCMOS technology and is available in a 28 pin DIP package. The on-chip computation unit includes a 97.5 ns 24×16-bit multiplier with a 40-bit accumulator, a 256×24-bit coefficient RAM, and a 256×16-bit data RAM. Three modes of operation allow the part to be used as a single FIR filter, a dual FIR filter, or a single adaptive FIR filter, with up to 256 taps/chip. In the adaptive FIR filter mode, the part performs the FIR filtering and LMS coefficient update operations for a single tap in 195 ns, permitting use of the part as a 19 kHz sampling rate, 256 tap adaptive FIR filter. Programmable DC tap, coefficient leakage, and adaptation coefficient parameters in the adaptive FIR mode allow the DSP56200 to be used in a wide variety of adaptive FIR filtering applications. The performance of the part in an echo canceller configuration will be presented. Typical applications of the part will also be described.

Narrow-band jammer suppression using an adaptive lattice filter

Abstract: It is well known that least-mean-squared (LMS) adaptive filtering can be used to suppress narrow-band jammers in a direct sequence spread spectrum receiver. To date, much of the work in this area has concentrated on the use of transversal adaptive filter structures. This paper demonstrates the performance of an adaptive lattice filter in this application. The reflection coefficients of the filter are adjusted using a gradient descent algorithm in an effort to minimize the mean-squared error and, in the process, suppress narrow-band interference. Experimental bit-error-rate performance curves are presented for 7- and 31- chip spreading sequences with a single tone jammer.