Showing papers on "Kernel adaptive filter published in 2000"

A new method for the nonlinear transformation of means and covariances in filters and estimators

Abstract: This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parametrize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Abstract: This paper proposes two gradient-based adaptive algorithms, called the least mean M estimate and the transform domain least mean M-estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M-estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method fur estimating the required thresholds in the M-estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses.

Multipath Mitigation of Continuous GPS Measurements Using an Adaptive Filter

Abstract: Though state-of-the-art dual-frequency receivers are employed in the continuous Global Positioning System (CGPS) arrays, the CGPS coordinate time series are typically very noisy due to the effects of atmospheric biases, multipath, receiver noise, and so on, with multipath generally being considered the major noise contributor. An adaptive finite-duration impulse response filter, based on a least-mean-square algorithm, has been developed to derive a relatively noise-free time series from the CGPS results. Furthermore, this algorithm is suitable for real-time applications. Numerical simulation studies indicate that the adaptive filters is a powerful signal decomposer, which can significantly mitigate multipath effects. By applying the filter to both pseudorange and carrier phase multipath sequences derived from some experimental GPS data, multipath models have been reliably derived. It is found that the best multipath mitigation strategy is forward filtering using data on two adjacent days, which reduces the standard deviations of the pseudorange multipath time series to about one fourth its magnitude before correction and to about half in the case of carrier phase. The filter has been successfully applied to the pseudorange multipath sequences derived from CGPS data. The benefit of this techniques is that the affected observable sequences can be corrected, and then these corrected observables can be used to improve the quality of the GPS coordinate results. © 2000 John Wiley & Sons, Inc.

Subband acoustic feedback cancellation in hearing aids

Abstract: A new subband feedback cancellation scheme is proposed, capable of providing additional stable gain without introducing audible artifacts. The subband feedback cancellation scheme employs a cascade of two narrow-band filters Ai(Z)and Bi(Z)ng with a fixed delay, instead of a single filter Wi(Z)and a delay to represent the feedback path in each subband. The first filter, Ai(Z), is called the training filter, and models the static portion of the feedback path in ith subband, including microphone, receiver, ear canal resonance, and other relatively static parameters. The training filter can be implemented as a FIR filter or as an IIR filter. The second filter, B?I?(Z), is called a tracking filter and is typically implemented as a FIR filter with fewer taps than the training filter. This second filter tracks the variations of the feedback path in the i?th? subband caused by jaw movement or objects close to the ears of the user.

A transform domain SVD filter for suppression of muscle noise artefacts in exercise ECG's

Abstract: The proposed filter assumes the noisy electrocardiography (ECG) to be modeled as a signal of deterministic nature, corrupted by additive muscle noise artefact. The muscle noise component is treated to be stationary with known second-order characteristics. Since noise-free ECG is shown to possess a narrow-band structure in discrete cosine transform (DCT) domain and the second-order statistical properties of the additive noise component is preserved due to the orthogonality property of DCT, noise abatement is easily accomplished via subspace decomposition in the transform domain. The subspace decomposition is performed using singular value decomposition (SVD), The order of the transform domain SVD filter required to achieve the desired degree of noise abatement is compared to that of a suboptimal Wiener filter using DCT. Since the Wiener filter assumes both the signal and noise structures to be statistical, with a priori known second-order characteristics, it yields a biased estimate of the ECG beat as compared to the SVD filter for a given value of mean-square error (mse). The filter order required for performing the subspace smoothing is shown to exceed a certain minimal value for which the mse profile of the SVD filter follows the minimum-mean-square error (mmse) performance warranted by the suboptimal Wiener filter. The effective filter order required for reproducing clinically significant features in the noisy ECG is then set by an upper bound derived by means of a finite precision linear perturbation model. A significant advantage resulting from the application of the proposed SVD filter lies in its ability to perform noise suppression independently on a single lead ECG record with only a limited number of data samples.

Characteristics of the Vold-Kalman order tracking filter

Abstract: The filter characteristics of the Vold-Kalman (1993, 1960, 1961) order tracking filter are presented. Both the frequency response as well as the time response and their time-frequency relationship have been investigated for different filter types and guidelines for optimum choice of filter parameters are presented. The Vold-Kalman filter allows for the high performance simultaneous tracking of orders in systems with multiple independent shafts. With this new filter and using multiple tacho references, waveforms, as well as amplitude and phase may be extracted without the beating interactions that are associated with conventional methods. Orders extracted as waveforms have no phase bias, and may hence be used for playback, synthesis and tailoring.

A computationally efficient frequency-domain LMS algorithm with constraints on the adaptive filter

Abstract: The frequency domain implementation of the LMS algorithm is attractive due to both the reduced computational complexity and the potential of faster convergence compared with the time domain implementation. Another advantage is the potential of using frequency-domain constraints on the adaptive filter, such as limiting its magnitude response or limiting the power of its output signal. This paper presents a computationally efficient algorithm that allows the incorporation of various frequency domain constraints into the LMS algorithm. A penalty function formulation is used with a steepest descent search to adapt the filter so that it converges to the new constrained minimum. The formulation of the algorithm is derived first, after which the use of some practical constraints with this algorithm and a simulation example for adaptive blind equalization are described.

Modulated filter banks with arbitrary system delay: efficient implementations and the time-varying case

Abstract: We present a new method for the design and implementation of modulated filter banks with perfect reconstruction. It is based on the decomposition of the analysis and synthesis polyphase matrices into a product of two different types of simple matrices, replacing the polyphase filtering part in a modulated filter bank. Special consideration is given to cosine-modulated as well as time-varying filter banks. The new structure provides several advantages. First of all, it allows an easy control of the input-output system delay, which can be chosen in single steps of input sampling rate, independent of the filter length. This property can be used in audio coding applications to reduce pre-echoes. Second, it results in a structure that is nearly twice as efficient as performing the polyphase filtering directly. Perfect reconstruction is a structurally inherent feature of the new formulation, even for nonlinear operations or time-varying coefficients. Hence, the structure is especially suited for the design of time-varying filter banks where both the number of bands as well as the prototype filters can be changed while maintaining perfect reconstruction and critical sampling. Further, a proof of effective completeness is given, and the design of equal magnitude-response analysis and synthesis filter banks is described. Filter design can be performed by nonconstrained optimization of the matrix coefficients according to a given cost function. Design and audio-coding application examples are given to show the performance of the new filter bank.

Method and apparatus for automatic volume control in an audio system

Abstract: An audio system ( 100 ) is provided with improved adaptive filter ( 206 ) to automatically adjust signal gain depending on the ambient noise level. The original music signal passes through a normalized adaptive filter ( 206 ), and is subtracted from the ambient room signal detected by a microphone ( 120 ), resulting in an error signal that is an estimate of the ambient noise. The error signal is used to update a set of adaptation coefficients so that the normalized adaptive filter more accurately simulates the room transfer function, resulting in an better estimate of the ambient noise. The audio system ( 100 ) is calibrated automatically upon initial use to determine adaptation coefficients and noise threshold level to prevent runaway gain. System parameters are adjusted using a controller ( 124 ) with a user-friendly interface ( 400 ).

Adaptive Fuzzy Filter and Its Application to Image Enhancement

Abstract: This chapter describes the design and evaluation of a novel adaptive fuzzy filter, and discusses its application to image enhancement. Most traditional edge detectors can perform well for uncorrupted images but are highly sensitive to impulse noise, so they can not work efficiently for blurred images. The proposed adaptive fuzzy filter consists of two major mechanisms: Adaptive Weighted Fuzzy Mean (AWFM) filter and Fuzzy Normed Inference System (FNIS) to realize the function of edge detection for smeared images. The membership functions of all fuzzy sets used in this filter can be adaptively determined for different images. Moreover, the adaptive fuzzy filter is capable of converting blurred edges to clear ones and suppressing noise at the same time. According to the experimental results, it works well in full range of random impulse noise probability and performs efficiently in the environment of mixed Gaussian impulse noise. This chapter also analytically evaluates the important properties of the filter to show its high performance in general cases.

Fast edge-preserving noise reduction for ultrasound images

Abstract: Edge-preserving noise reduction is an essential operation for computer-aided ultrasound image processing and understanding. This paper describes a novel filter which is a two-dimensional (2-D) extension of the one-dimensional (1-D) Savitzky-Golay filter. The new filter, referred to as the 2-D weighted Savitzky-Golay filter, is based on the least squares fitting of a polynomial function to image intensities. The performance of the proposed filter has been compared with that of the commonly used median filter in reducing speckle noise on a synthetic image and an ultrasound thyroid image. Experimental results indicate that on these particular examples, the new filter can achieve at least the same level of noise reduction and edge preservation as that of the median filter, but with far less computation time. Since its complexity scales linearly with the problem size, the new filter is suitable for filtering problems with large windows. In addition, its performance also shows to be less sensitive to the size of the filtering window compared to the median filter.

A generalized least-squares fault detection filter

Abstract: A fault detection and identification algorithm is determined from a generalization of the least-squares derivation of the Kalman filter. The objective of the filter is to monitor a single fault called the target fault and block other faults which are called nuisance faults. The filter is derived from solving a min–max problem with a generalized least-squares cost criterion which explicitly makes the residual sensitive to the target fault, but insensitive to the nuisance faults. It is shown that this filter approximates the properties of the classical fault detection filter such that in the limit where the weighting on the nuisance faults is zero, the generalized least-squares fault detection filter becomes equivalent to the unknown input observer where there exists a reduced-order filter. Filter designs can be obtained for both linear time-invariant and time-varying systems. Copyright © 2000 John Wiley & Sons, Ltd.

Emphasis of short-duration transient speech features

Abstract: A sound processor including a microphone (1), a pre-amplifier (2), a bank of N parallel filters (3), means for detecting short-duration transitions in the envelope signal of each filter channel, and means for applying gain to the outputs of these filter channels in which the gain is related to a function of the second-order derivative of the slow-varying envelope signal in each filter channel, to assist in perception of low-intensity short-duration speech features in said signal.

Method and apparatus for adaptive wall filtering in spectral doppler ultrasound imaging

Abstract: A method and an apparatus for adaptive wall (high-pass) filtering to remove low-frequency clutter in spectral Doppler I/Q data prior to FFT processing. The I/Q data is passed through a low-pass filter which rejects the flow frequency components above the clutter frequency range. The total power of the low-pass filter output is then computed. A system noise model is used to predict the mean system noise power in the low-pass filter output. The predicted mean noise power provides a noise threshold to gage how much clutter power is present in the current FFT packet. If no significant clutter is present, then wall filter selection logic will automatically select the lowest wall filter cutoff frequency stored in a filter coefficient LUT. If significant clutter power is present in the FFT packet, then the mean and variance of the clutter frequency over the FFT packet are estimated and then input into the filter selection logic, which selects the most suitable filter cutoff for the current clutter signal.

Evolving an adaptive digital filter

Abstract: One important feature of signal processing is coping with noise. In a non-adaptive filter, characteristics of the filter may be refined to remove noise. One method of achieving this is to use evolution to decide the filter characteristics. However, if the noise level is sufficient or the input signal is not of the required type for the output signal required, then a satisfactory output signal may not be achievable. To be able to achieve the required output signal for a wide range of input signals and noise, it is desirable to be able to adjust both the characteristics and the type of the filter. In this way the resulting filter may be said to be an adaptive filter. In this paper we propose an on-chip solution for an adaptive digital filter using an on-chip evolvable hardware method. We highlight a challenge within evolvable hardware for adaptive designs and that is to find efficient ways in which sufficient genetic material will be available to the evolution process. This problem appears when the evolution process is automatically restarted so as to adapt to a change in the environment.

Method and apparatus for compensating for intersymbol interference in a received signal

Abstract: A method and arrangement are proposed for compensating for intersymbol interference (ISI) in a multi-carrier transmission system. Compensation is based on the generation of an estimate of the ISI transient tail generated between consecutively transmitted symbols and subtracting this tail from the received signals. Each symbol includes a cyclic extension as prefix. The tail is generated from a tail portion isolated from the cyclic prefix. This tail portion is then used in a filter arrangement adapted to generate at least an estimate of the full transient signal. The filter function may be proceeded by a processing module for generating the initial conditions of the filter function from the transient signal portion. The full transient is then generated by inputting a predetermined value into the filter function. The processing module may be a second filter or processing arrangement adapted to perform calculations. The filter function may alternatively be an adaptive filter function that generates a full transient from the transient portion. The adaptive filter may be configured prior to use with a training sequence. Alternatively, or in addition, the adaptive filter function may also be adjusted using each received symbol to generate an error update signal.

Time adaptive denoising of single trial event-related potentials in the wavelet domain.

Abstract: We present a new wavelet-based method for single trial analysis of transient and time variant event-related potentials (ERPs). Expecting more accurate filter settings than achieved by other techniques (low-pass filter, a posteriori Wiener filter, time invariant wavelet filter), ERPs were initially balanced in time. By simulation, better filter performance could be established for test signals contaminated with either white noise or isospectral noise. To provide an example of real application, the method was applied to limbic P300 potentials (MTL-P300). As a result, variance of single trial MTL-P300s decreased, without restricting the corresponding mean. The proposed method can be regarded as an alternative for single-trial ERP analysis.

NNGD algorithm for neural adaptive filters

Abstract: A novel normalised nonlinear gradient descent (NNGD) algorithm for training neural adaptive feedforward filters is presented. The algorithm is based on minimisation of the instantaneous prediction error for contractive activation functions of a neuron, and provides an adaptive learning rate. Normalisation is performed via calculation of the product of the tap input power to the filter and the squared first derivative of the activation function of a neuron. The NNGD algorithm outperforms a gradient based algorithm for use in a neural adaptive filter, as well as the standard least mean squares (LMS) and normalised LMS algorithms. To support the analysis, simulation results on real speech are provided.

Higher order adaptive filter based predistortion for nonlinear distortion compensation of radio over fiber links

Abstract: The biggest limitation of radio over fiber (ROF) links in a wireless network is its limited dynamic range due to 'non-linear distortions' (NLD). In this paper a higher order adaptive filter based modeling and predistortion scheme is proposed to compensate this NLD. The filter is adapted from the distortions of vector-modulated symbols, so that no in-depth knowledge of physical link parameters is needed. Experimental and simulation results show that a third order filter accurately models the ROF link while a second order filter adequately compensates for the phase nonlinearity. The power handling capability of the laser diode is the upper limit in this approach.

Adaptive identification of nonlinear systems with application to chaotic communications

Abstract: This paper studies the nonlinear system identification problem in a noisy environment using an adaptive algorithm. In particular, nonlinear systems of the polynomial type are considered here. An improved least squares (ILS) objective function is used to reduce the estimation bias caused by measurement noise. Based on this ILS criterion, a novel adaptive filter is developed to track a time-varying polynomial system. Numerical simulations showed that the proposed adaptive algorithm was superior to the conventional identification technique. We applied this new adaptive filter to demodulate the signals of transmission in a chaotic multiuser spread spectrum (SS) communication system. It was observed that the new approach was effective in demodulating a SS signal, even at low signal-to-noise ratios (SNR's).

Adaptive inverse control algorithm for shock testing

Abstract: An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the characteristics of a new specimen. The control algorithm used is the adaptive inverse control method which approximates an inverse model of the loaded shaker with a finite impulse response adaptive filter, such that the reference input is reproduced at the shaker output. The standard filtered-x least mean square control structure used in the adaptive inverse control algorithm is modified to a block-processing structure, with the frequency-domain adaptive filter as the adaptation algorithm. Practical results show that the filtered-x frequency-domain adaptive filter control algorithm allows convergence of the shaker output to the assigned reference shock pulse.

Generalized inversion attack on nonlinear filter generators

Abstract: A nonlinear filter generator is a basic keystream generator for stream cipher applications consisting of a single linear feedback shift register whose output is filtered by a nonlinear combining function. A binary nonlinear filter generator is viewed as a finite input memory automaton with one binary input and one binary output. The generalized inversion attack on a binary nonlinear filter generator is developed and analyzed by the theory of critical branching processes. Its objective is to recover the unknown input sequence from a given segment of the output sequence, provided that the filter function is known. Unlike the inversion attack, which requires that the filter function be linear in the first or the last input variable, this attack can be applied for any filter function. Both theory and systematic experiments show that its time complexity remains close to 2^M , which is the time complexity of the inversion attack, where M denotes the input memory size in bits.

Theory and design of a class of cosine-modulated non-uniform filter banks

Abstract: In this paper, the theory and design of a class of PR cosine-modulated nonuniform filter bank is proposed. It is based on a structure previously proposed by Cox (1986), where the outputs of a uniform filter bank are combined or merged by means of the synthesis section of another filter bank with smaller channel number. Simplifications are imposed on this structure so that the design procedure can be considerably simplified. Due to the use of cosine modulated filter banks as the original and recombination filter banks, excellent filter quality and low design and implementation complexities can be achieved. Problems with these merging techniques such as spectrum inversion, equivalent filter representations and protrusion cancellation are also addressed. As the merging is performed after the decimation, the arithmetic complexity is lower than other conventional approaches. Design examples show that PR nonuniform filter banks with high stopband attenuation and low design and implementation complexities can be obtained by the proposed method.

Generalized inversion attack on nonlinear filter generators

Abstract: A nonlinear filter generator is a basic keystream generator for stream cipher applications consisting of a single linear feedback shift register whose output is filtered by a nonlinear combining function. A binary nonlinear filter generator is viewed as a finite input memory automaton with one binary input and one binary output. The generalized inversion attack on a binary nonlinear filter generator is developed and analyzed by the theory of critical branching processes. Its objective is to recover the unknown input sequence from a given segment of the output sequence, provided that the filter function is known. Unlike the inversion attack, which requires that the filter function be linear in the first or the last input variable, this attack can be applied for any filter function. Both theory and systematic experiments show that its time complexity remains dose to 2/sup M/, which is the time complexity of the inversion attack, where M denotes the input memory size in bits.

Robust adaptive recovery of spread-spectrum signals with short data records

Abstract: The problem under consideration is the adaptive reception of a multipath direct-sequence spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive impulsive noise. An SS receiver structure is proposed that consists of a vector of adaptive chip-based Hampel nonlinearities followed by an adaptive auxiliary-vector linear tap-weight filter. The nonlinear receiver front end adapts itself to the unknown prevailing noise environment providing robust performance over a wide range of underlying noise distributions. The adaptive auxiliary-vector linear tap-weight filter allows rapid SS interference suppression with a limited data record. Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.

Adaptive spatial filter

Abstract: A system for adaptively filtering an image so as to reduce a noise component associated with the image includes an image analyzer for determining image parameters related to the image. The system also includes a spatial filter, having an adjustable kernel responsive to the image parameters, for filtering the image sequence. The image analyzer manipulates the filter kernel as a function of the image parameters so that the system produces a filtered image, adaptable in real time, as a function of the unfiltered image, external rules, predetermined constraints, or combinations thereof. The spatial filter includes a time-invariant section and an adaptable section. The time-invariant section preferably applies a plurality of filters to the image, each of the filters having a distinct frequency response, so as to produce a plurality of distinct filtered outputs. The adaptable section scales each of the plurality of distinct filtered outputs with a corresponding distinct weighting value to produce a plurality of scaled filtered outputs, and combines the plurality of scaled filtered outputs to produce a composite filtered output.