Showing papers on "Adaptive filter published in 1995"

Adaptive median filters: new algorithms and results

Abstract: Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L/sub p/ filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters. >

Introduction to signal processing

Abstract: 1. Sampling and Reconstruction. 2. Quantization. 3. Discrete-Time Systems. 4. FIR Filtering and Convolution. 5. z-Transforms. 6. Transfer Functions. 7. Digital Filter Realizations. 8. Signal Processing Applications. 9. DFT/FFT Algorithms. 10. FIR Digital Filter Design. 11. IIR Digital Filter Design. 12. Interpolation, Decimation, and Oversampling. 13. Appendices. References. Index.

Wavelet filter evaluation for image compression

Abstract: Choice of filter bank in wavelet compression is a critical issue that affects image quality as well as system design. Although regularity is sometimes used in filter evaluation, its success at predicting compression performance is only partial. A more reliable evaluation can be obtained by considering an L-level synthesis/analysis system as a single-input, single-output, linear shift-variant system with a response that varies according to the input location module (2/sup L/,2/sup L/). By characterizing a filter bank according to its impulse response and step response in addition to regularity, we obtain reliable and relevant (for image coding) filter evaluation metrics. Using this approach, we have evaluated all possible reasonably short (less than 36 taps in the synthesis/analysis pair) minimum-order biorthogonal wavelet filter banks. Of this group of over 4300 candidate filter banks, we have selected and present here the filters best suited to image compression. While some of these filters have been published previously, others are new and have properties that make them attractive in system design. >

Space-time adaptive processing for airborne radar

Abstract: Advanced airborne radar systems are required to detect targets in the presence of both clutter and jamming. Ground clutter is extended in both angle and range, and is spread in Doppler frequency because of the platform motion. Space-time adaptive processing (STAP) refers to the simultaneous processing of the signals from an array antenna during a multiple pulse coherent waveform. STAP can provide improved detection of targets obscured by mainlobe clutter, defection of targets obscured by sidelobe clutter, and detection in combined clutter and jamming environments. Fully adaptive STAP is impractical for reasons of computational complexity and estimation with limited data, so partially adaptive approaches are required. The paper presents a taxonomy of partially adaptive STAP approaches that are classified according to the type of preprocessor, or equivalently, by the domain in which adaptive weighting occurs. Analysis of the rank of the clutter covariance matrix in each domain provides insight and conditions for preprocessor design.

Displacement structure: theory and applications

Abstract: In this survey paper, we describe how strands of work that are important in two different fields, matrix theory and complex function theory, have come together in some work on fast computational algorithms for matrices with what we call displacement structure. In particular, a fast triangularization procedure can be developed for such matrices, generalizing in a striking way an algorithm presented by Schur (1917) [J. Reine Angew. Math., 147 (1917), pp. 205–232] in a paper on checking when a power series is bounded in the unit disc. This factorization algorithm has a surprisingly wide range of significant applications going far beyond numerical linear algebra. We mention, among others, inverse scattering, analytic and unconstrained rational interpolation theory, digital filter design, adaptive filtering, and state-space least-squares estimation.

The fast affine projection algorithm

Abstract: This paper discusses a new adaptive filtering algorithm called fast affine projections (FAP). FAP's key features include LMS like complexity and memory requirements (low), and RLS like convergence (fast) for the important case where the excitation signal is speech. Another of FAP's important features is that it causes no delay in the input or output signals. In addition, the algorithm is easily regularized resulting in robust performance even for highly colored excitation signals. The combination of these features make FAP an excellent candidate for the adaptive filter in the acoustic echo cancellation problem. A simple, low complexity numerical stabilization method for the algorithm is also introduced.

An adaptive optimal-kernel time-frequency representation

Abstract: Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal-dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with characteristics that change with time. The time-frequency representation developed in the present paper, based on a signal-dependent radially Gaussian kernel that adapts over time, surmounts these difficulties. The method employs a short-time ambiguity function both for kernel optimization and as an intermediate step in computing constant-time slices of the representation. Careful algorithm design provides reasonably efficient computation and allows on-line implementation. Certain enhancements, such as cone-kernel constraints and approximate retention of marginals, are easily incorporated with little additional computation. While somewhat more expensive than fixed kernel representations, this new technique often provides much better performance. Several examples illustrate its behavior on synthetic and real-world signals. >

A delayless subband adaptive filter architecture

Abstract: Some adaptive signal processing applications, such as wideband active noise control and acoustic echo cancellation, involve adaptive filters with hundreds of taps. The computational burden associated with these long adaptive filters precludes their use for many low-cost applications. In addition, adaptive filters with many taps may also suffer from slow convergence, especially if the reference signal spectrum has a large dynamic range. Subband techniques have been previously developed for adaptive filters to solve these problems. However, the conventional approach is ruled out for many applications because delay is introduced into the signal path. The paper presents a new type of subband adaptive filter architecture in which the adaptive weights are computed in subbands, but collectively transformed into an equivalent set of wideband filter coefficients. In this manner, signal path delay is avoided while retaining the computational and convergence speed advantages of subband processing. An additional benefit accrues through a significant reduction of aliasing effects. An example of the general technique is presented for a 32-subband design using a polyphase FFT implementation. For this example, the number of multiplies required are only about one-third that of a conventional full band design with zero delay, and only slightly greater than that of a conventional subband design with 16 ms delay. >

CS-ACELP speech compression system with adaptive pitch prediction filter gain based on a measure of periodicity

Abstract: A speech coding system employing an adaptive codebook model of periodicity is augmented with a pitch-predictive filter (PPF). This PPF has a delay equal to the integer component of the pitch-period and a gain which is adaptive based on a measure of periodicity of the speech signal. In accordance with an embodiment of the present invention, speech processing systems which include a first portion comprising an adaptive codebook and corresponding adaptive codebook amplifier and a second portion comprising a fixed codebook coupled to a pitch filter, are adapted to delay the adaptive codebook gain; determine the pitch filter gain based on the delayed adaptive codebook gain, and amplify samples of a signal in the pitch filter based on said determined pitch filter gain. The adaptive codebook gain is delayed for one subframe. The pitch filter gain equals the delayed. adaptive codebook gain, except when the adaptive codebook gain is either less than 0.2 or greater than 0.8., in which cases the pitch filter gain is set equal to 0.2 or 0.8, respectively.

Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness

Abstract: The performance of signal enhancement systems based on adaptive filtering is highly dependent on the quality of the noise reference. In the LMS algorithm, signal leakage into the noise reference leads to signal distortion and poor noise cancellation. The origin of the problem lies in the fact that LMS decorrelates the signal estimate with the noise reference, which, in the case of signal leakage, makes little sense. An algorithm is proposed that decorrelates the signal estimate with a "signal-free" noise estimate, obtained by adding a symmetric filter to the classical structure. The symmetric adaptive decorrelation (SAD) algorithm no longer makes a distinction between signal and noise and is therefore a signal separator rather than a noise canceler. Stability and convergence are of the utmost importance in adaptive algorithms and hence are carefully studied. Apart from limitations on the adaptation constants, stability around the desired solution can only be guaranteed for a subclass of signal mixtures. Furthermore, the decorrelation criterion does not yield a unique solution, and expressions for the "phantom" solutions are derived. Simulations with short FIR filters confirm the predicted behavior. >

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

Abstract: The paper presents a robust variable step size LMS-type algorithm with the attractive property of achieving a small final misadjustment while providing fast convergence at early stages of adaptation. The performance of the algorithm is not affected by the presence of noise. Approximate analysis of convergence and steady state performance for zero-mean stationary Gaussian inputs and a nonstationary optimal weight vector is provided. Simulation results clearly indicate its superior performance for stationary cases. For the nonstationary environment, the algorithm provides performance equivalent to that of the regular LMS algorithm.

Analysis of the filtered-X LMS algorithm

Abstract: The presence of a transfer function in the auxiliary-path following the adaptive filter and/or in the error-path, as in the case of active noise control, has been shown to generally degrade the performance of the LMS algorithm. Thus, the convergence rate is lowered, the residual power is increased, and the algorithm can even become unstable. To ensure convergence of the algorithm, the input to the error correlator has to be filtered by a copy of the auxiliary-error-path transfer function. This paper presents an analysis of the filtered-X LMS algorithm using stochastic methods. The influence of off-line and on-line estimation of the error-path filter on the algorithm is also investigated. Some derived bounds and predicted dynamic behavior are found to correspond very well to simulation results.

Nonlinear adaptive prediction of nonstationary signals

Abstract: We describe a computationally efficient scheme for the nonlinear adaptive prediction of nonstationary signals whose generation is governed by a nonlinear dynamical mechanism. The complete predictor consists of two subsections. One performs a nonlinear mapping from the input space to an intermediate space with the aim of linearizing the input signal, and the other performs a linear mapping from the new space to the output space. The nonlinear subsection consists of a pipelined recurrent neural network (PRNN), and the linear section consists of a conventional tapped-delay-line (TDL) filter. The nonlinear adaptive predictor described is of general application. The dynamic behavior of the predictor is demonstrated for the case of a speech signal; for this application, it is shown that the nonlinear adaptive predictor outperforms the traditional linear adaptive scheme in a significant way. >

Simulation-based word-length optimization method for fixed-point digital signal processing systems

Abstract: Word-length optimization and scaling software that utilizes the fixed-point simulation results using realistic input signal samples is developed for the application to general, including nonlinear and time-varying, signal processing systems. Word-length optimization is conducted to minimize the hardware implementation cost while satisfying a fixed-point performance measure. In order to minimize the computing time, signal grouping and efficient search methods are developed. The search algorithms first determine the minimum bound of the word-length for an individual group of signals and then try to find out the cost-optimal solution by using either exhaustive or heuristic methods.

An adaptive speckle suppression filter for medical ultrasonic imaging

Abstract: An adaptive smoothing technique for speckle suppression in medical B-scan ultrasonic imaging is presented. The technique is based on filtering with appropriately shaped and sized local kernels. For each image pixel, a filtering kernel, which fits to the local homogeneous region containing the processed pixel, is obtained through a local statistics based region growing technique. The performance of the proposed filter has been tested on the phantom and tissue images. The results show that the filter effectively reduces the speckle while preserving the resolvable details. The simulation results are presented in a comparative way with two existing speckle suppression methods. >

Reducing acoustic feedback in hearing aids

Abstract: Five techniques for reducing acoustic feedback in hearing aids were investigated: an adaptive notch filter, three previously described methods for adaptive feedback cancellation, and a novel method for feedback cancellation with adaptation during quiet intervals. Through real-time implementations, these techniques were assessed for added stable gain and sound quality. Test results showed the novel system to provide the best overall performance. >

Transform-domain adaptive filters: an analytical approach

Abstract: Transform-domain adaptive filters refer to LMS filters whose inputs are preprocessed with a unitary data-independent transformation followed by a power normalization stage. The transformation is typically chosen to be the discrete Fourier transform (DFT), although other transformations, such as the cosine transform (DCT), the Hartley transform (DHT), or the Walsh-Hadamard transform, have also been proposed in the literature. The resulting algorithms are generally called DFT-LMS, DCT-LMS, etc. This preprocessing improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter and, as a consequence, ameliorates its convergence speed. In this paper, we start with a brief intuitive explanation of transform-domain algorithms. We then analyze the effects of the preprocessing performed in DFT-LMS and DCT-LMS for first-order Markov inputs. In particular, we show that for Markov-1 inputs of correlation parameter /spl rho//spl isin/[0,1], the eigenvalue spread after DFT and power normalization tends to (1+/spl rho/)l(1-/spl rho/) as the size of the filter gets large, whereas after DCT and power normalization, it reduces to (1+/spl rho/). For comparison, the eigenvalue spread before transformation is asymptotically equal to (1+/spl rho/)/sup 2//(1-/spl rho/)/sup 2/. The analytical method used in the paper provides additional insight into how the algorithms work and is expected to extend to other input signal classes and other transformations. >

Adaptive averaging methodology for handoffs in cellular systems

Abstract: The purpose of this paper is to show how information in signal strength measurements can be exploited to improve the quality of handoff decisions, for both large and small cells. Averaging of signal strength fluctuations is required. This leads to the following tradeoff problem for the averaging interval for the signal strength measurements. If the interval is too short, the fading fluctuations are not sufficiently smoothed out. If the interval is too long, delay in handoff increases. With this tradeoff in mind, we present a method to adaptively change the averaging interval. The method is based on estimating the maximum Doppler frequency, f D , as a means to obtain mobile velocity, the key to the tradeoff. A method used for estimating f D from the squared deviations of the signal envelope is outlined. Exact analysis for the f D estimate as a function of squared deviations of the logarithmically compressed signal envelope in Rayleigh fading is presented. An extension of the algorithm for robustness in a Rician fading environment is given. Sensitivity issues of the estimates are considered. An adaptive scheme for optimal averaging is outlined.

Adaptive Processing: The Least Mean Squares Approach with Applications in Transmission

Abstract: Transmission Systems Theory of Adaptive Transversal (FIR) Filters Implementation Considerations Tracking the Time-Variations Adaptive Recursive (IIR) Filters The Case of Independent Input Vectors

Practical transform coding of multispectral imagery

Abstract: We present an implementable three dimensional terrain adaptive transform based bandwidth compression technique for multispectral imagery. The algorithm exploits the inherent spectral and spatial correlations in the data. The compression technique is based on Karhunen-Loeve transformation for spectral decorrelation followed by the standard JPEG algorithm for coding the resulting spectrally decorrelated eigen images. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near-lossless at about 5:1 CR to visually lossy beginning at about 30:1 CR. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral correlation transformation as a function of the variation of the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a alternate spatial coding procedure. The significant practical advantage of this proposed approach is that it is based on the standard and highly developed JPEG compression technology. >

Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter

Abstract: The bootstrap filter is an algorithm for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples that are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: It may be applied to any state transition or measurement model. A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter. A preliminary investigation of an application of the bootstrap filter to an exoatmospheric engagement with non-Gaussian measurement errors is also given.

A maximal invariant framework for adaptive detection with structured and unstructured covariance matrices

Abstract: We introduce a framework for exploring array detection problems in a reduced dimensional space by exploiting the theory of invariance in hypothesis testing This involves calculating a low-dimensional basis set of functions called the maximal invariant, the statistics of which are often tractable to obtain, thereby making analysis feasible and facilitating the search for tests with some optimality property Using this approach, we obtain a locally most powerful invariant test for the unstructured covariance case and show that all invariant tests can be expressed in terms of the previously published Kelly's generalized likelihood ratio (GLRT) and Robey's adaptive matched filter (AMF) test statistics Applying this framework to structured covariance matrices, corresponding to stochastic interferers in a known subspace, for which the GLRT is unavailable, we obtain the maximal invariant and propose several new invariant detectors that are shown to perform as well or better than existing ad-hoc detectors These invariant tests are unaffected by most nuisance parameters, hence the variation in the level of performance is sharply reduced This framework facilitates the search for such tests even when the usual GLRT is unavailable >

Sparse equalization for real-time digital underwater acoustic communications

Abstract: Due to the very long reverberation time of many ocean channels, the size of the adaptive filters required for conventional equalization becomes large, rendering the computational complexity of the adaptive receiver unacceptable for many cases of practical interest. To overcome this problem we exploit the natural sparseness of the reverberation pattern. By focusing only on those intervals which contain a significant portion of the signal energy, the sparse equalization method provides data detection using a minimum complexity adaptive receiver subject to an upper bound on the signal estimation error. Experimental results demonstrate an order of magnitude reduction in computational complexity with a negligible loss in performance.

A refined gamma MAP SAR speckle filter with improved geometrical adaptivity

Abstract: A modified version of the refined gamma maximum-a-posteriori (RGMAP) speckle filter, which is found in the literature, is presented. The traditional RGMAP speckle filter first defects contours belonging to step edges and thin linear structures, then applies the RGMAP filter to local statistics extracted from rectangular masks that do not cross image contours. The proposed modified RGMAP (MRGMAP) filter first exploits local operators belonging to the odd-symmetric filter category employed by RGMAP to detect image segments, then it computes local statistics over areas that are not necessarily rectangular, but are subsets of the image segments having any possible shape. Therefore, MRGMAP enhances the RGMAP ability in exploiting shape adaptive windowing near image contours, where speckle is not fully developed. The MRGMAP computation time is estimated to be of the same magnitude of that of the original RGMAP, the latter depending on the number of filter categories being employed. The qualitative and quantitative results of the MRGMAP filter applied to real SAR images are satisfactory as the filter seems to be effective in speckle removal whereas it retains edge sharpness and subtle details. However, tests on simulated SAR images must still be performed in order to provide definitive evidence supporting MRGMAP effectiveness. Since MRGMAP typically removes image structures featuring a constant reflectivity gradient, this filter is not particularly suitable for image enhancement in human photo-interpretation. MRGMAP can be rather employed as a preprocessing module in a computer-based SAR image classification procedure based on segment mean value analysis. >

Analog reflection topology building blocks for adaptive microwave signal processing applications

Abstract: The synthesis and realization of an analog-phase shifter, delay line, attenuator, and group delay synthesizer-are presented. These variable control devices are all implemented using the same generic single stage reflection topology. The optimum conditions of operation have been determined and the corresponding group delay behaviors have been investigated to produce simple design equations. As proof-of-concepts, monolithic technology has been used to realize an X-band, phase shifter, delay line, and attenuator. Hybrid technology has been used to realize an L-band, group-delay synthesizer. Because of the high levels of performance measured, these control devices are ideally suited for use as general building blocks in adaptive signal processing applications, including large phased array applications. >

Elimination of AC interference in electrocardiogram using IIR notch filter with transient suppression

Abstract: A technique for suppressing the transient states of IIR notch filter is investigated. This technique uses the vector projection to find better initial values for notch filters. When a notch or comb filter is used to eliminate power line (AC) interference in the recording of electrocardiograms (ECG), the performance of the notch filter with transient suppression is better than that of the conventional notch filter with arbitrary initial condition. The improvements with this technique are at the cost of additional computation load at the beginning of filtering. >

A simple adaptive first-order differential microphone

Abstract: As communication devices become more portable and used in any environment, the acoustic pick-up by electroacoustic transducers will require the combination of small compact transducers and signal-processing to allow high quality communication. This paper covers the design and implementation of a novel adaptive first-order differential microphone. The self-optimization is based on minimizing the microphone output under the constraint that the solitary null for first order systems is located in the rear-half plane. The constraint is simply realized by the judicious subtraction of time-delayed outputs from two closely-spaced omnidirectional microphones. Although the solution presented does not maximize the signal-to-noise ratio, it can significantly improve the signal-to-noise ratio in certain acoustic fields.

Adaptive arrays and MLSE equalization

Abstract: In digital cellular communication systems, receivers are designed to combat the problems of fading, time dispersion, and interference. Separately, these problems can be solved using antenna diversity, equalization, and adaptive array processing, respectively. Joint solutions have been proposed which combine adaptive array processing with either linear equalization (LE) or decision feedback equalization (DFE). However, LE is sensitive to spectral nulls and DFE has the problem of decision error feedback. In this paper, an algorithm is proposed which combines adaptive array processing with MLSE equalization to mitigate fading, time dispersion, and interference. The optimal metric for the MLSE structure is derived. The receiver reduces to well known forms for special cases. D-AMPS simulation results illustrate the potential performance of the proposed algorithm, including the case when the fading on the different antennas is correlated.

Isolation of resonance in acoustic backscatter from elastic targets using adaptive estimation schemes

Abstract: The problem of underwater target detection and classification from acoustic backscatter is the central focus of this paper. It has been shown that at certain frequencies the acoustic backscatter from elastic targets exhibits certain resonance behavior which closely relates to the physical properties of the target such as dimension, thickness, and composition. Several techniques in both the time domain and frequency domain have been developed to characterize the resonance phenomena in acoustic backscatter from spherical or cylindrical thin shells. The purpose of this paper is to develop an automated approach for identifying the presence of resonance in the acoustic backscatter from an unknown target by isolating the resonance part from the specular contribution. An adaptive transversal filter structure is used to estimate the specular part of the backscatter and consequently the error signal would provide an estimate of the resonance part. An important aspect of this scheme Lies in the fact that it does not require an underlying model for the elastic return. The adaptation rule is based upon fast Recursive Least Squares (RLS) learning. The approach taken in this paper is general in the sense that it can be applied to targets of unknown geometry and thickness and, further, does not require any a priori information about the target and/or the environment. Test results on acoustic data are presented which indicate the effectiveness of the proposed approach.

Digital filter bank design quadratic-constrained formulation

Abstract: Formulate the filter bank design problem as an quadratic-constrained least-squares minimization problem. The solution of the minimization problem converges very quickly since the cost function as well as the constraints are quadratic functions with respect to the unknown parameters. The formulations of the perfect-reconstruction cosine-modulated filter bank, of the near-perfect-reconstruction pseudo-QMF bank, and of the two-channel biorthogonal linear-phase filter bank are derived using the proposed approach. Compared with other design methods, the proposed technique yields PR filter banks with much higher stopband attenuation. The proposed technique can also be extended to design multidimensional filter banks. >