Showing papers on "Adaptive filter published in 1992"

A CFAR adaptive matched filter detector

Abstract: An adaptive algorithm for radar target detection using an antenna array is proposed. The detector is derived in a manner similar to that of the generalized likelihood-ratio test (GLRT) but contains a simplified test statistic that is a limiting case of the GLRT detector. This simplified detector is analyzed for performance to signals on boresight, as well as when the signal direction is misaligned with the look direction. >

A variable step size LMS algorithm

Abstract: A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady-state behavior of the algorithm are analyzed. The results reduce to well-known results when specialized to the constant-step-size case. Simulation results are presented to support the analysis and to compare the performance of the algorithm with the usual LMS algorithm and another variable-step-size algorithm. They show that its performance compares favorably with these existing algorithms. >

A filter bank for the directional decomposition of images: theory and design

Abstract: The authors introduce a directionally oriented 2-D filter bank with the property that the individual channels may be critically sampled without loss of information. The passband regions of the component filters are wedge-shaped and thus provide directional information. It is shown that these filter bank outputs may be maximally decimated to achieve a minimum sample representation in a way that permits the original signal to be exactly reconstructed. The authors discuss the theory for directional decomposition and reconstruction. In addition, implementation issues are addressed where realizations based on both recursive and nonrecursive filters are considered. >

Frequency-domain and multirate adaptive filtering

Abstract: An overview is presented of several frequency-domain adaptive filters that efficiently process discrete-time signals using block and multirate filtering techniques. These algorithms implement a linear convolution that is equivalent to a block time-domain adaptive filter, or they generate a circular convolution that is an approximation. Both approaches exploit the computational advantages of the FFT. Subband adaptive filtering is also briefly described. Here the input data are first processed by a bank of narrowband bandpass filters that are approximately nonoverlapping. The transformed signals are then decimated by a factor that depends on the degree of aliasing that can be tolerated, resulting in a large computational savings. Several performance issues are considered, including convergence properties and computational complexities of the adaptive algorithms and the effects of circular convolution and aliasing on the converged filter coefficients. >

Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation

Abstract: An exact analysis of the critically subsampled two-band modelization scheme is given, and it is demonstrated that adaptive cross-filters between the subbands are necessary for modelization with small output errors. It is shown that perfect reconstruction filter banks can yield exact modelization. These results are extended to the critically subsampled multiband schemes, and important computational savings are seen to be achieved by using good quality filter banks. The problem of adaptive identification in critically subsampled subbands is considered and an appropriate adaptation algorithm is derived. The authors give a detailed analysis of the computational complexity of all the discussed schemes, and experimentally verify the theoretical results that are obtained. The adaptive behavior of the subband schemes that were tested is discussed. >

Adaptive Filtering in Subbands with Critical Sampling: Analysis, Experiments, and Application to Acoustic

Abstract: Adaptive filtering in subbands is a new technique for the real-time identification of large impulse responses like the ones encountered in acoustic echo cancellation. This tech- nique generally allows computational savings as well as better convergence behavior. We give first an exact analysis of the critically subsampled two band modelization scheme. We demonstrate that adaptive cross-filters between the subbands are necessary for modeliza- tion with small output errors; moreover, we show that perfect reconstruction filter banks can yield exact modelization. We ex- tend those results to the critically subsampled multiband schemes, and we show that important computational savings can be achieved by using good quality filter banks. Then we consider the problem of adaptive identification in critically sub- sampled subbands, and we derive an appropriate adaptation algorithm. We give a detailed analysis of the computational complexity of all the discussed schemes, and we verify experi- mentally the theoretical results that we have obtained. Finally, we discuss the adaptive behavior of the subband schemes that we have tested. We generally observe some degradation of the convergence performance in comparison with conventional schemes; however, the overall performance could be acceptable in practical use.

Order statistics in digital image processing

Abstract: A family of nonlinear filters based on order statistics is presented. A mathematical tool derived through robust estimation theory, order statistics has allowed engineers to develop nonlinear filters with excellent robustness properties. These filters are well suited to digital image processing because they preserve the edges and the fine details of an image much better than conventional linear filters. The probabilistic and deterministic properties of the best known and most widely used filter in this family, the median filter, are discussed. In addition, the authors consider filters that, while not based on order statistics, are related to them through robust estimation theory. A table that ranks nonlinear filters under a variety of performance criteria is included. Most of the topics treated are very active research areas, and the applications are varied, including HDTV, multichannel signal processing of geophysical and ECG/EEG data, and a variety of telecommunications applications. >

Neural-network-based adaptive matched filtering for QRS detection

Abstract: The authors have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. They use an ANN adaptive whitening filter to model the lower frequencies of the electrocardiogram (ECG) which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. The authors developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. The detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5% with this approach, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method. >

An adaptive algorithm for mel-cepstral analysis of speech

Abstract: The authors describe a mel-cepstral analysis method and its adaptive algorithm. In the proposed method, the authors apply the criterion used in the unbiased estimation of log spectrum to the spectral model represented by the mel-cepstral coefficients. To solve the nonlinear minimization problem involved in the method, they give an iterative algorithm whose convergence is guaranteed. Furthermore, they derive an adaptive algorithm for the mel-cepstral analysis by introducing an instantaneous estimate for gradient of the criterion. The adaptive mel-cepstral analysis system is implemented with an IIR adaptive filter which has an exponential transfer function, and whose stability is guaranteed. The authors also present examples of speech analysis and results of an isolated word recognition experiment. >

Simulation of 3-D random rough surface by 2-D digital filter and fourier analysis

Abstract: 2-D FIR filters are applied to the generation of 3-D random surfaces in this paper. The circularly symmetric low-pass filters are firstly employed to generate the isotropic random surfaces with no restrictions on the shape of ACF. To simulate real rough surfaces, however, one has to generate the surfaces having an expected autocorrelation function and height distribution, which requires determining the filter coefficients corresponding to the specified ACF. A spectrum analysis approach is developed in this paper to compute the filter coefficients and the FFT procedure provides an efficient way to implement the filter. The Johnson translator system of distribution is introduced into the generation procedure of non-Gaussian surface with a required ACF. The analysis indicates that the 2-D FIR filters used in this paper are mathematically identical with the 2-D MA time series model which simulates the ACF within whole correlation regions.

A weighted least squares algorithm for quasi-equiripple FIR and IIR digital filter design

Abstract: It has been demonstrated by several authors that if a suitable frequency response weighting function is used in the design of a finite impulse response (FIR) filter, the weighted least squares solution is equiripple. The crux of the problem lies in the determination of the necessary least squares frequency response weighting function. A novel iterative algorithm for deriving the least squares frequency response weighting function which will produce a quasi-equiripple design is presented. The algorithm converges very rapidly. It typically produces a design which is only about 1 dB away from the minimax optimum solution in two iterations and converges to within 0.1 dB in six iterations. Convergence speed is independent of the order of the filter. It can be used to design filters with arbitrarily prescribed phase and amplitude response. >

Evaluation of an adaptive beamforming method for hearing aids.

Abstract: In this paper evaluations of a two-microphone adaptive beamforming system for hearing aids are presented. The system, based on the constrained adaptive beamformer described by Griffiths and Jim [IEEE Trans. Antennas Propag. AP-30, 27-34 (1982)], adapts to preserve target signals from straight ahead and to minimize jammer signals arriving from other directions. Modifications of the basic Griffiths-Jim algorithm are proposed to alleviate problems of target cancellation and misadjustment that arise in the presence of strong target signals. The evaluations employ both computer simulations and a real-time hardware implementation and are restricted to the case of a single jammer. Performance is measured by the spectrally weighted gain in the target-to-jammer ratio in the steady state. Results show that in environments with relatively little reverberation: (1) the modifications allow good performance even with misaligned arrays and high input target-to-jammer ratios; and (2) performance is better with a broadside array with 7-cm spacing between microphones than with a 26-cm broadside or a 7-cm endfire configuration. Performance degrades in reverberant environments; at the critical distance of a room, improvement with a practical system is limited to a few dB.

System and method for improved speech acquisition for hands-free voice telecommunication in a noisy environment

Abstract: Systems and methods for improved speech acquisition are disclosed including a plurality of linearly arrayed sensors to detect spoken input and to output signals in response thereto, a beamformer connected to the sensors to cancel a preselected noise portion of the signals to thereby produce a processed signal, and a speech recognition system to recognize the processed signal and to respond thereto. The beamformer may also include an adaptive filter with enable/disable circuitry for selectively training the adaptive filter a predetermined period of time. A highpass filter may also be used to filter a preselected noise portion of the sensed signals before the signals are forwarded to the beamformer. The speech recognition system may include a speaker independent base which is able to be adapted by a predetermined amount of training by a speaker, and which system includes a voice dialer or a speech coder for telecommunication.

Digital shaping filters for reducing machine vibration

Abstract: A digital shaping filter used to shape the input to digitally controlled flexible plants is derived from an input preshaping technique to reduce residual vibration in the plant output. Results from simulations show that these filters reduce plant residual vibration to zero. In addition, an arbitrary rate digital shaping filter is derived that allows the user to select any sampling rate for the digital system. The examination of these filters in the digital Z domain reveals them to be digital notch filters that are robust to vibration-mode parameter shifts. >

Time-domain filter bank analysis: a new design theory

Abstract: The authors present a new time-domain approach for the analysis and design of a broad class of general analysis/synthesis systems based on M-band filter banks. They derive a set of time-domain conditions for reconstruction which can be used directly in a filter bank design procedure. The general and unrestricted nature of this framework allows for the design of many useful banks. In addition to the complete derivation of the time-domain conditions, they also describe the associated filter bank design procedure and a number of design examples are included. >

Quadratic filters for signal processing

Abstract: Polynomial (or Volterra) filters are introduced, and the quadratic filters are presented as the simplest example of such filters. The principle aspects and properties of quadratic filters are derived in the framework of the discrete Volterra expansion. Fixed as well as adaptive filters are considered in one-dimensional and multidimensional environments. Such issues as design and efficient realizations are thoroughly addressed, and standard and advanced adaptation algorithms are presented. Several examples of signal processing applications requiring quadratic filters are discussed. >

The behavior of a multiple channel active control system

Abstract: The convergence behavior of an adaptive feedforward active control system is studied. This adjusts the outputs of a number of secondary sources to minimize a cost function comprising a combination of the sum of mean-square signals from a number of error sensors (the control error) and the sum of the mean-square signals fed to the secondary sources (the control effect). A steepest descent algorithm which performs this function is derived and analyzed. It is shown that some modes not only converge slowly but also require an excessive control effort for complete convergence. This ill-conditioned behavior can be controlled by the proper choice of the cost function minimized. Laboratory experiments using a 16-loudspeaker 32-microphone control system to control the harmonic sound in an enclosure are presented. The behavior of the practical system is accurately predicted from the theoretical analysis of the adaptive algorithm. The effect of errors in the assumed transfer matrix used by the steepest descent algorithm is briefly discussed. >

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input : comparison with signal averaging techniques

Abstract: An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.<>

Applications of simulated annealing for the design of special digital filters

Abstract: The authors describe the salient features of using a simulated annealing (SA) algorithm in the context of designing digital filters with coefficient values expressed as the sum of power of two. A procedure for linear phase digital filter design, using this algorithm, is presented and tested, yielding results as good as those for known optimal methods. The algorithm is then applied to the design of Nyquist filters, optimizing at the same time both frequency response and intersymbol interference, and to the design of cascade form finite-impulse-response (FIR) filters. The drawback of using SA is that the computation time is on the order of 1-2 h for each filter design, on the Sun 3/60. However, this was more than compensated by the versatility of the new algorithm, which can be used to design filters with multiple constraints. >

Adaptive Active Control of Combustion Instabilities

Abstract: It is shown in this article that adaptive control algorithms may be used to suppress combustion instabilities in turbulent combustors. In a typical configuration a signal delivered by a sensor monitoring the flame serves as input to a numerical adaptive filter which sends a control signal through an actuator into the combustion chamber. The controller coefficients are updated at the sampling rate using a global response signal. The controller automatically finds optimal coefficients to control the different unstable modes of oscillation. In addition it adapts to evolutions in operating conditions such as a continuous sweep of air flow rate and a shift in the global equivalence ratio. Under certain conditions a secondary mode appears when the control system is operating. While this oscillation occurs as a result of a destabilizing effect of the control loop its amplitude remains limited. The theoretical background is developed and used to explain typical implementation problems such as the filter ...

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

Abstract: An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials. >

Adaptive inverse filters for stereophonic sound reproduction

Abstract: A general theoretical basis for the design of adaptive digital filters used for the equalization of the response of multichannel sound reproduction systems is described. The approach is applied to the two-channel case and then extended to deal with arbitrary numbers of channels. The intention is to equalize not only the response of the loudspeakers and the listening room but also the crosstalk transmission from right loudspeaker to left ear and vice versa. The formulation is a generalization of the Atal-Schroeder crosstalk canceler. However, the use of a least-squares approach to the digital filter design and of appropriate modeling delays potentially allows the effective equalization of nonminimum phase components in the transmission path. A stochastic gradient algorithm which facilitates the adaptation of the digital filters to the optimal solution, thereby providing the possibility of designing the filters in situ, is presented. Some experimental results for the two-channel case are given. >

An adaptive morphological filter for image processing

Abstract: Novel types of opening operator (NOP) and closing operator (NCP) are proposed. An adaptive morphological filter is then constructed on the basis of the NOP and NCP. The filter can remove any details consisting of fewer pixels than a given number N, while preserving the other details. Efficient algorithms are also developed for the implementation of the NOP and NCP. >

Active noise control system

Abstract: A active noise control system is described for attenuating an undesirable noise that containing multiple noise components having different frequencies. The system includes a configuration of signal generators and corresponding paired adaptive filters. Each signal generator produces a generator output signal containing one or more sinusoidal signal components, with each signal component having a frequency corresponding to a respective one of the noise frequency components. Separate signal generators are used to produce those sinusoidal components having frequencies corresponding to noise component that are adjacent with respect to their frequencies. Each adaptive filter operates on the generator output signal produced by its corresponding paired signal generator in accordance with its filtering characteristics to provide a respective filtered output signal. The filtered output signals from each adaptive filter are summed to obtain an output canceling signal, which drives an actuator to produce output canceling waves that are superpositioned with the undesirable noise. An error sensor measures the level of residual noise resulting from the superposition of the canceling waves and the undesirable noise, and provides an error signal for adapting the characteristics of the adaptive filters so as to minimize the level of residual noise.

Image processing system and method employing adaptive filtering to provide improved reconstruction of continuous tone images from halftone images including those without a screen structure

Abstract: An image processing system is provided to convert halftone images to continuous tone images. It employs an adaptive filter which processes successive pixels in an input halftone image. The adaptive filter employs a filter that is selected under feedback control from a plurality of filter sets each having a plurality of filters. The halftone image is also low-pass filtered to generate a first approximation image (FAI). A spatial gradient value is computed for each pixel in the FAI. A control operates the adaptive filter to apply one of the predetermined filters to the current pixel as a function of the associated pixel spatial gradient. An output image from the adaptive filter in a first iteration of the filtering procedure can then be applied to the input of the adaptive filter for a second adaptive filtering iteration. Pixel gradients for the second iteration are computed from the image output from the first iteration. A predetermined number of iterations are performed and the image output from the last iteration is a continuous tone image for system output.

Adaptive determination of filter scales for edge detection

Abstract: The authors suggest a regularization method for determining scales for edge detection adaptively for each site in the image plane. Specifically, they extend the optimal filter concept of T. Poggio et al. (1984) and the scale-space concept of A. Witkin (1983) to an adaptive scale parameter. To avoid an ill-posed feature synthesis problem, the scheme automatically finds optimal scales adaptively for each pixel before detecting final edge maps. The authors introduce an energy function defined as a functional over continuous scale space. Natural constraints for edge detection are incorporated into the energy function. To obtain a set of optimal scales that can minimize the energy function, a parallel relaxation algorithm is introduced. Experiments for synthetic and natural scenes show the advantages of the algorithm. In particular, it is shown that this system can detect both step and diffuse edges while drastically filtering out the random noise. >

Canonical piecewise-linear approximations

Abstract: The canonical representation of piecewise-linear functions is considered as a universal approximation scheme of multivariate functions. Meanwhile, two universal approximation schemes in terms of combinations of univariate canonical piecewise-linear functions are proposed. The discussion supports the application of these schemes in mapping networks, e.g. neural networks or adaptive nonlinear filters. >

Lagrange multiplier approaches to the design of two-channel perfect-reconstruction linear-phase FIR filter banks

Abstract: The authors present two approaches to the design of two-channel perfect-reconstruction linear-phase finite-impulse-response (FIR) filter banks. Both approaches analyze and design the impulse responses of the analysis filter bank directly. The synthesis filter bank is then obtained by simply changing the signs of odd-order coefficients in the analysis filter bank. The approach deals with unequal-length filter banks. By designing the lower length filters first, one can take advantage of the fact that the number of variables for designing the higher length filters is more than the number of perfect-reconstruction constraint equations. The second approach generalizes the first, and covers the design for all parts of linear phase perfect reconstruction constraint equations. >

Stable and efficient lattice algorithms for adaptive IIR filtering

Abstract: Previous attempts at applying lattice structures to adaptive infinite-impulse-response (IIR) filtering have met with gradient computations of O(N/sup 2/) complexity. To overcome this computational burden, two new lattice-based algorithms are proposed for adaptive IIR filtering and system identification, with both algorithms of O(N) complexity. The first algorithm is a reinterpretation of the Steiglitz-McBride method (1965), while the second is a variation on the output error method. State space models are employed to make the derivations transparent, and the methods can be extended to other parameterizations if desired. The set of possible stationary points of the algorithms is shown to be consistent with the convergent points obtained from the direct-form versions of the Steiglitz-McBride and output error methods, whose properties are well studied. The derived algorithms are as computationally efficient as existing direct-form based algorithms, while overcoming the stability problems associated with time-varying direct-form filters. >

Adaptive baseline wander removal in the ECG: Comparative analysis with cubic spline technique

Abstract: Baseline wandering is a classical problem in electrocardiogram (ECG) records that generally produces artifactual data when measuring ECG parameters. The authors present a cascade adaptive filter for removing the baseline wander and preserving the low-frequency components of the ECG. This cascade adaptive filter works in two stages. The first stage is an adaptive notch filter at zero frequency. The second stage is an adaptive impulse correlated filter that, using a QRS detector, estimates the ECG signal correlated with the QRS occurrence. In this way, all the signal components correlated with the QRS complex are preserved. The authors analyze the frequency response of the filter, showing that the filter can be seen as a comb filter without the DC lobe. The method was applied to ECG signals from the MIT-BIH database and its performance was compared with the cubic spline approach. The method can remove baseline wander in real time without needing to calculate the isoelectric levels, while preserving the low-frequency ECG clinical information. >