Showing papers on "Adaptive filter published in 1997"

Adaptive Filtering: Algorithms and Practical Implementation

Abstract: From the Publisher: Adaptive Filtering: Algorithms and Practical Implementation is a concise presentation of adaptive filtering, covering as many algorithms as possible while avoiding adapting notations and derivations related to the different algorithms. Furthermore, the book points out the algorithms which really work in a finite-precision implementation, and provides easy access to the working algorithms for the practicing engineer. Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.

A robust variable step-size LMS-type algorithm: analysis and simulations

Abstract: A number of time-varying step-size algorithms have been proposed to enhance the performance of the conventional LMS algorithm. Experimentation with these algorithms indicates that their performance is highly sensitive to the noise disturbance. This paper presents a robust variable step-size LMS-type algorithm providing fast convergence at early stages of adaptation while ensuring small final misadjustment. The performance of the algorithm is not affected by existing uncorrelated noise disturbances. An approximate analysis of convergence and steady-state performance for zero-mean stationary Gaussian inputs and for nonstationary optimal weight vector is provided. Simulation results comparing the proposed algorithm to current variable step-size algorithms clearly indicate its superior performance for cases of stationary environments. For nonstationary environments, our algorithm performs as well as other variable step-size algorithms in providing performance equivalent to that of the regular LMS algorithm.

Phase-only adaptive nulling with a genetic algorithm

Abstract: This paper describes a new approach to adaptive phase-only nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase shifters to minimize the total output power. Using small adaptive phase values results in minor deviations in the beam steering direction and small perturbations in the sidelobe level in addition to constraining the search space of the genetic algorithm. Various results are presented to show the advantages and limitations of this approach, in general, the genetic algorithm proves to be better than previous phase-only adaptive algorithms.

Reduced-rank adaptive filtering

Abstract: A novel rank reduction scheme is introduced for adaptive filtering problems. This rank reduction method uses a cross-spectral metric to select the optimal lower dimensional subspace for reduced-rank adaptive filtering as a function of the basis vectors of the full-rank space.

Application of filter sharpening to cascaded integrator-comb decimation filters

Abstract: A new architecture for the implementation of high-order decimation filters is described. It combines the cascaded integrator-comb (CIC) multirate filter structure with filter sharpening techniques to improve the filter's passband response. This allows the first-stage CIC decimation filter to be followed by a fixed-coefficient second-stage filter, rather than a programmable filter, thereby achieving a significant hardware reduction over existing approaches. Furthermore, the use of fixed-coefficient filters in place of programmable-coefficient filters improves the overall throughput rate. The resulting architecture is well suited for single-chip VLSI implementation with very high data-sample rates. We discuss an example with specifications suitable for use in a wideband satellite communication subband tuner system and for signal analysis.

Adaptive tracking of linear time-variant systems by extended RLS algorithms

Abstract: We exploit the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm. Two particular forms of the extended RLS algorithm are considered: one pertaining to a system identification problem and the other pertaining to the tracking of a chirped sinusoid in additive noise. For both of these applications, experiments are presented that demonstrate the tracking superiority of the extended RLS algorithms compared with the standard RLS and least-mean-squares (LMS) algorithms.

Speech representation and transformation using adaptive interpolation of weighted spectrum: vocoder revisited

Abstract: A simple new procedure called STRAIGHT (speech transformation and representation using adaptive interpolation of weighted spectrum) has been developed. STRAIGHT uses pitch-adaptive spectral analysis combined with a surface reconstruction method in the time-frequency region, and an excitation source design based on phase manipulation. It preserves the bilinear surface in the time-frequency region and allows for over 600% manipulation of such speech parameters as pitch, vocal tract length, and speaking rate, without further degradation due to the parameter manipulation.

Adaptive filters employing partial updates

Abstract: In some adaptive filtering applications, the least-mean-square (LBIS) algorithm may be too computationally- and memory-intensive to implement. In this paper, we analyze two adaptive algorithms that update only a portion of the coefficients of the adaptive filter per iteration. These algorithms use decimated versions of the error and regressor signals, respectively. Simulations verify the accuracy of the analyzes, and the robustness of the algorithms is also explored.

A robust mixed-norm adaptive filter algorithm

Abstract: We propose a new member of the family of mixed-norm stochastic gradient adaptive filter algorithms for system identification applications based upon a convex function of the error norms that underlie the least mean square (LMS) and least absolute difference (LAD) algorithms. A scalar parameter controls the mixture and relates, approximately, to the probability that the instantaneous desired response of the adaptive filter does not contain significant impulsive noise. The parameter is calculated with the complementary error function and a robust estimate of the standard deviation of the desired response. The performance of the proposed algorithm is demonstrated in a system identification simulation with impulsive and Gaussian measurement noise.

Adaptive watermarking in the DCT domain

Abstract: An adaptive watermarking technique is introduced in this work. A regional perceptual classifier is employed to assign a noise sensitivity index to each region. The watermark is inserted in the original image according to this index by using block DCT. The detection of the watermark is designed to achieve a desired false alarm probability.

Interference mitigation in spread spectrum communication systems using time-frequency distributions

Abstract: The capability of the time-frequency distributions (TFDs) to properly represent a single as well as multiple component signals in time and frequency permits the application of a new approach for interference excision in spread spectrum communication systems. The instantaneous frequency (IF) estimate from the TFD is used to construct a finite impulse response filter that reduces the interference power with a minimum possible distortion of the desired signal. The proposed technique is therefore a case of open-loop adaptive filtering. Three- and five-coefficient zero-phase excision filters are considered. Closed-form expressions of the improvement of SNR at the receiver correlator output using the TFD-based adaptive filtering are derived for two extreme cases of time-varying interferers, namely, those of fixed frequency sinusoids and randomly changing instantaneous frequencies. Simulation results including the bit error rates are presented for both swept and frequency hopping jammers.

Inverse filtering criteria for CDMA systems

Abstract: Linear blind CDMA receivers are derived using inverse filtering criteria. The receiver parameters are directly obtained without explicit estimation of the system/channel. Both synchronous and asynchronous cases are addressed, and multipath distortions are explicitly considered and compensated for. The approach is based on minimizing the receiver's output energy subject to appropriate constraints. Similar approaches have been used before in multiuser systems but without considering multipath distortions. Batch and adaptive blind algorithms are derived that are near-far resistant and do not require knowledge of the interfering users' codes. Global convergence is shown, optimality and performance issues are discussed, and some illustrative simulations are presented.

New digital linear filters for Hankel J0 and J1 transforms

Abstract: The numerical evaluation of certain integral transforms is required for the interpretation of some geophysical exploration data. Digital linear filter operators are widely used for carrying out such numerical integration. It is known that the method of Wiener–Hopf minimization of the error can be used to design very efficient, short digital linear filter operators for this purpose. We have found that, with appropriate modifications, this method can also be used to design longer filters. Two filters for the Hankel J0 transform (61-point and 120-point operators), and two for the Hankel J1 transform (47-point and 140-point operators) have been designed. For these transforms, the new filters give much lower errors compared to all other known filters of comparable, or somewhat longer, size. The new filter operators and some results of comparative performance tests with known integral transforms are presented. These filters would find widespread application in many numerical evaluation problems in geophysics.

Video postfiltering with motion-compensated temporal filtering and/or spatial-adaptive filtering

Abstract: A postfiltering process for improving the appearance of a video image includes motion compensated temporal filtering and spatial adaptive filtering. For each target pixel being filtered, the temporal filtering uses multiple motion vectors and one or more pixel values for a prior frame to determine one of more reference values for the target filter. In one embodiment, a weighted average of multiple motion vectors for blocks near or containing the target pixel value provides a filter vector that points to a pixel value in the prior frame. This pixel value is a reference value for the target pixel value and is combined with the target pixel value in a filter operation. Alternatively, multiple motion vectors for blocks near or containing the target pixel value point to pixel values in the prior frame that are averaged to determine a reference value for the target pixel value. In each alternative, the weighting for the average is selected according to the position of the target pixel value. The spatial filtering determines a dynamic range of pixel values in a smaller block containing the target pixel value and a dynamic range of pixel values in a larger block containing the target pixel value. The two dynamic ranges suggest the image context of the target pixel, and an appropriate spatial filter for the target pixel is selected according to the suggested context.

Evaluation and design of filters using a Taylor series expansion

Abstract: We describe a new method for analyzing, classifying, and evaluating filters that can be applied to interpolation filters as well as to arbitrary derivative filters of any order. Our analysis is based on the Taylor series expansion of the convolution sum. Our analysis shows the need and derives the method for the normalization of derivative filter weights. Under certain minimal restrictions of the underlying function, we are able to compute tight absolute error bounds of the reconstruction process. We demonstrate the utilization of our methods to the analysis of the class of cubic BC-spline filters. As our technique is not restricted to interpolation filters, we are able to show that the Catmull-Rom spline filter and its derivative are the most accurate reconstruction and derivative filters, respectively, among the class of BC-spline filters. We also present a new derivative filter which features better spatial accuracy than any derivative BC-spline filter, and is optimal within our framework. We conclude by demonstrating the use of these optimal filters for accurate interpolation and gradient estimation in volume rendering.

A secondary path modeling technique for active noise control systems

Abstract: A secondary path modeling technique for active noise control systems is developed for both on-line and off-line modeling with faster convergence and higher modeling accuracy. The optimum delay for the adaptive prediction error filter to reduce the interference in system modeling is equal to the length of the impulse response of the secondary path being modeled.

Waveform selective probabilistic data association

Abstract: An adaptive, waveform selective probabilistic data association (WSPDA) algorithm for tracking a single target in clutter is presented. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking subsystem equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimize the average total mean-square tracking error at the next time step. Semiclosed form solutions are given to the local (one-step-ahead) adaptive waveform selection problem for the case of one-dimensional target motion. A simple simulation example is given to compare the performance of a tracking system using a WSFDA based tracking filter with that of a conventional system with a fixed waveform shape and probabilistic data association (PDA) tracking filter.

Multi-channel speech enhancement in a car environment using Wiener filtering and spectral subtraction

Abstract: This paper presents a multichannel-algorithm for speech enhancement for hands-free telephone systems in cars. This new algorithm takes advantage of the special noise characteristics in fast driving cars. The incoherence of the noise allows to use adaptive Wiener filtering in the frequencies above a theoretically determined frequency. Below this frequency a smoothed spectral subtraction (SSS) is used to get an improved noise suppression. The algorithm yields better results in noise reduction with significantly less distortions and artificial noise than spectral subtraction or Wiener filtering alone.

Improving practical space-time adaptive radar

Abstract: This paper discusses the incorporation of nonhomogeneity detection with space-time adaptive processing to improve the formation of adaptive weights in practical adaptive airborne radar. We examine the problem of improving interference covariance matrix estimation in real-world environments and discuss several approaches for integrating nonhomogeneity detection with space-time adaptive processing. We use measured airborne data from the Rome Laboratory Multichannel Airborne Radar Measurements Program to illustrate key points.

Clutter rejection filters in color flow imaging: a theoretical approach

Abstract: A general class of linear clutter rejection filters is described, covering the commonly used filter types including FIR/IIR filters with linear initialization, as well as regression filters, where the clutter component is estimated by least square curve fitting. The filter can be described by a complex valued matrix, and a frequency response is defined. However, in contrast to a time invariant filter, the general linear filter may create frequency components which are not present in the input signal. This produces bias in the velocity and velocity spread estimates. It is shown that the clutter filter effect on the autocorrelation estimates can be described by a frequency domain transfer function, but unlike time invariant filters, the transfer function is different for each temporal lag of the autocorrelation function. Using a two dimensional (axial and temporal dimension) model of the received signal, the bias in velocity and velocity spread is quantified, both for the autocorrelation algorithm and the time shift cross-correlation estimator. Theoretical expressions, as well as numerical examples are given.

Subspace selection for partially adaptive sensor array processing

Abstract: This paper introduces a cross-spectral metric for subspace selection and rank reduction in partially adaptive minimum variance array processing. The counter-intuitive result that it is suboptimal to perform rank reduction via the selection of the subspace formed by the principal eigenvectors of the array covariance matrix is demonstrated. A cross-spectral metric is shown to be the optimal criterion for reduced-rank Wiener filtering.

The Dolph-Chebyshev Window: A Simple Optimal Filter

Abstract: Analyzed data for numerical prediction can be effectively initialized by means of a digital filter. Computation time is reduced by using an optimal filter. The construction of optimal filters involves the solution of a nonlinear minimization problem using an iterative procedure. In this paper a simple filter based on the Dolph‐Chebyshev window, which has properties similar to those of an optimal filter, is described. It is shown to be optimal for an appropriate choice of parameters. It has an explicit analytical expression and is easily implemented. Its effectiveness is demonstrated by application to Richardson’s forecast: the initial pressure tendency is reduced from 145 hPa pe r6ht o 20.9 hPa per 6 h. Use of the filter is not restricted to initialization; it may also be applied as a weak constraint in four-dimensional data assimilation.

Adaptive mammographic image enhancement using first derivative and local statistics

Abstract: This paper proposes an adaptive image enhancement method for mammographic images, which is based on the first derivative and the local statistics. The adaptive enhancement method consists of three processing steps. The first step is to remove the film artifacts which may be misread as microcalcifications. The second step is to compute the gradient images by using the first derivative operators. The third step is to enhance the important features of the mammographic image by adding the adaptively weighted gradient images. Local statistics of the image are utilized for adaptive realization of the enhancement, so that image details can be enhanced and image noises can be suppressed. The objective performances of the proposed method were compared with those by the conventional image enhancement methods for a simulated image and the seven mammographic images containing real microcalcifications. The performance of the proposed method was also evaluated by means of the receiver operating characteristics (ROC) analysis for 78 real mammographic images with and without microcalcifications.

Blind adaptive interference suppression for the near-far resistant acquisition and demodulation of direct-sequence CDMA signals

Abstract: Two key operations required of a receiver in a direct-sequence (DS) code division multiple access (CDMA) system are the timing acquisition of transmissions that are starting up or have lost synchronization, and the demodulation of transmissions that have been acquired. The reliability of both these operations is limited by multiple-access interference, especially for conventional matched filter-based methods, whose performance displays an interference floor and is vulnerable to the near-far problem. Recent work has shown that, provided timing information is available for a given transmission, it can be demodulated reliably using blind or training-sequence-based adaptive interference suppression techniques. These techniques are near-far resistant, unlike the matched filter demodulator, and do not require explicit knowledge of the interference parameters, unlike nonadaptive multiuser detectors. In this paper, we present a blind adaptive interference suppression technique for joint acquisition and demodulation, which has the unique feature that the output of the acquisition process is not simply the timing of the desired transmission, but a near-far resistant demodulator that implicitly accounts for knowledge of the timing and amplitudes of all transmissions to suppress the multiple-access interference. The only knowledge required by the scheme is that of the desired transmission's signature sequence, so that it is amenable to a decentralized implementation. On the other hand, it can be efficiently implemented as a centralized scheme in which the bulk of the computations for the adaptation are common to all transmissions that need to be acquired or demodulated.

Sequential blind signal extraction in order specified by stochastic properties

Abstract: A new neural-network adaptive algorithm is proposed for performing extraction of independent source signals from a linear mixture of them. Using a suitable nonlinear Hebbian learning rule and a new deflation technique, the developed neural network is able to extract the source signals (sub-Gaussian and/or super-Gaussian) one-by-one with specified order according to their stochastic properties, namely, in decreasing order of absolute normalised kurtosis. The validity and performance of the algorithm are confirmed through extensive computer simulations.

Color image processing using adaptive multichannel filters

Abstract: New adaptive filters for color image processing are introduced and analyzed. The proposed adaptive methodology constitutes a unifying and powerful framework for multichannel signal processing. Using the proposed methodology, color image filtering problems are treated from a global viewpoint that readily yields and unifies previous, seemingly unrelated, results. The new filters utilize Bayesian techniques and nonparametric methodologies to adapt to local data in the color image. The principles behind the new filters are explained in detail. Simulation studies indicate that the new filters are computationally attractive and have excellent performance.

Design of an adaptive antenna array for tracking the source of maximum power and its application to CDMA mobile communications

Abstract: This paper presents an alternative method of adaptive beamforming. Under an assumption that the desired signal is large enough compared to each of interfering signals at the receiver, which is preconditionally achieved in code division multiple access (CDMA) mobile communications by the chip correlator, the proposed technique provides for a suboptimal beam pattern that increases the signal-to-noise/signal-to-interference ratio (SNR/SIR) and eventually increases the capacity of the communication channel. The main advantages of the new technique are: (1) the procedure requires neither reference signals nor a training period; (2) the signal intercoherency does not affect the performance or complexity of the entire procedure; and (3) the total amount of computation is tremendously reduced compared to that of most conventional beamforming techniques such that the suboptimal beam pattern is produced at every snapshot on a real-time basis. In fact, the total computational load for generating a new set of weights including the update of an N-by-N autocovariance matrix is O(3N/sup 2/+12N). It can further be reduced down to O(11N) by approximating the matrix with the instantaneous signal vector.

Signal adaptive filtering method and signal adaptive filter for reducing blocking effect and ringing noise

Abstract: A signal adaptive filtering method is disclosed for reducing a blocking effect and ringing noise of an image data. A gradient of the image data is calculated for each pixel of the image data. Then, the gradient data of each pixel is compared with a global threshold value (T g ) which is determined based on a predetermined quantization step size (Q), and global edge map information of the pixel is generated. Meanwhile, the gradient data of each pixel is compared with a local threshold value (T n ) determined for each block having a predetermined size, and local edge map information of the pixel is generated. An OR operation is performed with respect to the global edge map information and the local edge map information to generate binary edge map information. Then, a predetermined sized filter window is applied to determine whether edges are present in the binary edge map information within the filter window. Afterwards, the image data pixel values of the corresponding filter window are filtered, pixel by pixel, by using predetermined first weighted values to generate a first new pixel value if it is determined that edges are not present. The image data pixel values within the corresponding filter window are filtered, pixel by pixel, by using predetermined second weighted values to generate a second new pixel value if it is determined that edges are present within the window. No filtering is performed if the pixel located at the center of the filter window represents an edge.

Bi-iteration SVD subspace tracking algorithms

Abstract: We present a class of fast subspace tracking algorithms that arise from a straightforward extension of Bauer's (1957) classical bi-iteration to the sequential processing case. The bi-iteration concept has an unexpected potential in subspace tracking. Our new bi-SVD subspace trackers are well structured and show excellent convergence properties. They outperform the TQR-SVD subspace tracking algorithm. Detailed comparisons confirm our claims. An application to rank and data adaptive signal reconstruction is also discussed.

A direct equalization method

Abstract: The unshielded twisted pair can be used as a transmission media for local distribution networks. To maintain a high transmission throughput, an analog or a digital adaptive channel equalizer is usually required in the receiver to minimize the effect of inter-symbol interference. Under the observation that the high sampling rate high precision A/D and subsequent digital adaptive signal processing is an expensive approach, a direct equalization method, where the equalizer is implemented in the transmitter, is proposed for symmetrical twisted pair transmission channels. This direct equalization method can also be applied to the analog equalization approach for reduced system complexity.