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Signal Processing, IEEE Transactions on

Issue 7 • Date July 2000

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Displaying Results 1 - 25 of 34
  • Corrections to "Zolotarev polynomials and optimal fir filters"

    Publication Year: 2000 , Page(s): 2171
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (36 KB)  

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  • Set-theoretic estimation based on a priori knowledge of the noise distribution

    Publication Year: 2000 , Page(s): 2150 - 2156
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    A new algorithm for estimation of a linear-in-parameters model is developed and tested by simulation. The method is based on the assumption of independent, identically distributed noise samples with a triangular density function. Such a noise model well approximates the symmetrically distributed sources of noise frequently encountered in practice, and the inclusion of a distribution assumption allows the computation of a pseudo-mean estimate to complement the set solution. The proposed algorithm recursively incorporates incoming observations with decreasing computational complexity as the number of updates increases. Simulations demonstrate that the algorithm has very favorable convergence rates and estimation accuracy and is very robust to deviations from the assumed noise properties. Comparisons with other set-theoretic algorithms and with conventional RLS are given View full abstract»

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  • Some properties of symmetric-antisymmetric orthonormal multiwavelets

    Publication Year: 2000 , Page(s): 2161 - 2163
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (104 KB)  

    We analyze the discrete multiwavelet transform using symmetric-antisymmetric orthonormal multifilters (SAOMFs) and prove that for any even-length SAOMF, we can always find an odd-length SAOMF such that the implementation of discrete multiwavelet transform using either the even-length or the odd-length SAOMF produces identical output for a given input signal if the sum/difference prefilter is chosen View full abstract»

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  • Transmitter redundancy for blind estimation and equalization of time- and frequency-selective channels

    Publication Year: 2000 , Page(s): 2029 - 2043
    Cited by:  Papers (30)  |  Patents (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    Joint mitigation of time- and frequency-selective fading is an important and challenging problem in mobile communications. Relying on transmitter-induced redundancy, we propose novel channel estimation and symbol recovery approaches for blind identification and equalization of time- and frequency-selective channels, where the time variation is modeled deterministically by a basis expansion. The resulting statistical algorithm enables the usage of a single antenna, dispenses with channel disparity conditions of existing approaches, and allows channel order overestimation. In addition, new deterministic algorithms for generalized OFDM systems are introduced that produce reliable estimates with few data points at high SNR's. Simulations illustrate the approaches developed View full abstract»

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  • Vector ARMA estimation: a reliable subspace approach

    Publication Year: 2000 , Page(s): 2092 - 2104
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB)  

    A parameter estimation method for finite-dimensional multivariate linear stochastic systems, which is guaranteed to produce valid models approximating the true underlying system in a computational time of a polynomial order in the system dimension, is presented. This is achieved by combining the main features of certain stochastic subspace identification techniques with sound matrix Schur restabilizing procedures and multivariate covariance fitting, both of which are formulated as linear matrix inequality problems. All aspects of the identification method are discussed, with an emphasis on the two issues mentioned above, and examples of the overall performance are provided for two different systems View full abstract»

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  • Inferring the eigenvalues of covariance matrices from limited, noisy data

    Publication Year: 2000 , Page(s): 2083 - 2091
    Cited by:  Papers (25)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB)  

    The eigenvalue spectrum of covariance matrices is of central importance to a number of data analysis techniques. Usually, the sample covariance matrix is constructed from a limited number of noisy samples. We describe a method of inferring the true eigenvalue spectrum from the sample spectrum. Results of Silverstein (1986), which characterize the eigenvalue spectrum of the noise covariance matrix, and inequalities between the eigenvalues of Hermitian matrices are used to infer probability densities for the eigenvalues of the noise-free covariance matrix, using Bayesian inference. Posterior densities for each eigenvalue are obtained, which yield error estimates. The evidence framework gives estimates of the noise variance and permits model order selection by estimating the rank of the covariance matrix. The method is illustrated with numerical examples View full abstract»

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  • Closed-loop linear model validation and order estimation using polyspectral analysis

    Publication Year: 2000 , Page(s): 1965 - 1974
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (296 KB)  

    Suppose that we perform closed-loop linear system identification using polyspectral analysis given noisy time-domain input-output measurements. In this setup, it is assumed that various disturbances affecting the system are zero-mean stationary Gaussian, whereas the closed-loop system operates under an external (possibly noisy) non-Gaussian input. The closed-loop system must be stable, but it is allowed to be unstable in the open loop. Various techniques have been proposed for system identification using polyspectral analysis. Having obtained a model, how do we know if the fitted model is “good?” This paper is devoted to the problem of statistical model validation using polyspectral analysis. We propose simple statistical tests based on the estimated polyspectrum (integrated bispectrum and/or integrated trispectrum) of an output error signal or the estimated cross-polyspectrum between the external reference and the output error signal. Model order estimation is performed by repeatedly using the model validation procedure. Computer simulation examples are presented in support of the proposed approaches View full abstract»

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  • Two-dimensional equirotational stack subspace fitting with an application to uniform rectangular arrays and ESPRIT

    Publication Year: 2000 , Page(s): 1902 - 1914
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (332 KB)  

    A two-dimensional (2-D) multiple invariance technique for computing signal subspaces for uniform rectangular arrays (URAs) of size M×N sensors is introduced. The method is based on a multiple maximum overlap configuration of the sensors in the array with m×n subarrays of (M-m+1)×(N-n+1) sensors each. We exploit the fact that the stacked subspace of the subarray sensor output signals admits a two-level equirotational stack parametrization. We introduce a TLS-type algorithm for estimating the parameters of this equirotational stack subspace model. Based on this method of equirotational stack subspace fitting, the overall array signal subspace can be estimated with a much higher accuracy than with conventional unstructured SVD and TLS techniques. Detailed experiments validate the theoretical results. We propose a variant of 2-D ESPRIT based on equirotational stack subspace fitting. This 2-D equirotational stack ESPRIT (2-D ES-ESPRIT) algorithm clearly outperforms conventional unstructured variants of 2-D ESPRIT. A detailed comparison with 2-D unitary ESPRIT is presented View full abstract»

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  • An adaptive receiver with an antenna array for channels with correlated non-Gaussian interference and noise using the SAGE algorithm

    Publication Year: 2000 , Page(s): 2172 - 2175
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    An adaptive receiver that uses multiple antennas to provide diversity against fading is developed for operation in an impulsive noise environment. The noise components at each sensor are assumed to be correlated. A mixture of multivariate Gaussian distributions is used to model the noise. Using a training sequence, model parameters are estimated by iterative procedures derived from the expectation-maximization (EM) algorithm. These estimated parameters are then used in a likelihood ratio test to recover the transmitted signals. Simulations show that the proposed adaptive receiver is robust, and near-optimum performance can be achieved when sufficient training data is available View full abstract»

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  • Mean likelihood frequency estimation

    Publication Year: 2000 , Page(s): 1937 - 1946
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (296 KB)  

    Estimation of signals with nonlinear as well as linear parameters in noise is studied. Maximum likelihood estimation has been shown to perform the best among all the methods. In such problems, joint maximum likelihood estimation of the unknown parameters reduces to a separable optimization problem, where first, the nonlinear parameters are estimated via a grid search, and then, the nonlinear parameter estimates are used to estimate the linear parameters. We show that a grid search can be avoided by using the mean likelihood estimator for estimating the unknown nonlinear parameters and how its performance can be made equivalent to that of the maximum likelihood estimator (MLE). The mean likelihood estimator requires computation of a multidimensional integral. However, using the concepts of importance sampling, we obtain the mean likelihood estimate without using integration. The technique is computationally far less burdensome than the direct maximum likelihood method but performs just as well. Simulation examples for estimating frequencies of multiple sinusoids in noise are given. The general technique can be applied to a large class of nonlinear regression problems View full abstract»

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  • Hopfield neural network based algorithms for image restoration and reconstruction. I. Algorithms and simulations

    Publication Year: 2000 , Page(s): 2105 - 2118
    Cited by:  Papers (24)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB)  

    In our previous work, the eliminating-highest error (EHE) criterion was proposed for the modified Hopfield (1982) neural network (MHNN) for image restoration and reconstruction. The performance of the MHNN is considerably improved by the EHE criterion as shown in many simulations. In inspiration of revealing the insight of the EHE criterion, in this paper, we first present a generalized updating rule (GUR) of the MHNN for gray image recovery. The stability properties of the GUR are given. It is shown that the neural threshold set up in this GUR is necessary and sufficient for energy decrease with probability one at each update. The new fastest-energy-descent (FED) criterion is then proposed parallel to the EHE criterion. While the EHE criterion is shown to achieve the highest probability of correct transition, the FED criterion achieves the largest amount of energy descent. In image restoration, the EHE and FED criteria are equivalent. A group of new algorithms based on the EHE and FED criteria is set up. A new measure, the correct transition rate (CTR), is proposed for the performance of iterative algorithms. Simulation results for gray image restoration show that the EHE (FED) based algorithms obtained the best visual quality and highest SNR of recovered images, took much smaller number of iterations, and had higher CTR. The CTR is shown to be a rational performance measure of iterative algorithms and predict quality of recovered images View full abstract»

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  • A statistical and physical mechanisms-based interference and noise model for array observations

    Publication Year: 2000 , Page(s): 2044 - 2056
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (372 KB)  

    A statistical noise model is developed from mathematical modeling of the physical mechanisms that generate noise in communication receivers employing antenna arrays. Such models have been lacking for cases where the antenna observations may be statistically dependent from antenna to antenna. The model is developed by generalizing an approach for single antenna cases suggested by Middleton (1967, 1974, 1976, 1977). The model derived here is applicable to a wide variety of physical situations. The focus is primarily on problems defined by Middleton to be Class A interference. The number of noise sources in a small region of space is assumed to be Poisson distributed, and the emission times are assumed to be uniformly distributed over a long time interval. Finally, an additive Gaussian background component is included to represent the thermal noise that is always present in real receivers View full abstract»

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  • Temporal BYY learning for state space approach, hidden Markov model, and blind source separation

    Publication Year: 2000 , Page(s): 2132 - 2144
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    The temporal Bayesian Yang-Yang (TBYY) learning has been presented for signal modeling in a general state space approach, which provides not only a unified point of view on the Kalman filter, hidden Markov model (HMM), independent component analysis (ICA), and blind source separation (BSS) with extensions, but also further advances on these studies, including a higher order HMM, independent HMM for binary BSS, temporal ICA (TICA), and temporal factor analysis for real BSS without and with noise. Adaptive algorithms are developed for implementation and criteria are provided for selecting an appropriate number of states or sources. Moreover, theorems are given on the conditions for source separation by linear and nonlinear TICA. Particularly, it has been shown that not only non-Gaussian but also Gaussian sources can also be separated by TICA via exploring temporal dependence. Experiments are also demonstrated View full abstract»

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  • Power-law shot noise and its relationship to long-memory α-stable processes

    Publication Year: 2000 , Page(s): 1883 - 1892
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    We consider the shot noise process, whose associated impulse response is a decaying power-law kernel of the form tβ/2-1 . We show that this power-law Poisson model gives rise to a process that, at each time instant, is an α-stable random variable if β<1. We show that although the process is not α-stable, pairs of its samples become jointly α-stable as the distance between them tends to infinity. It is known that for the case β>1, the power-law Poisson process has a power-law spectrum. We show that, although in the case β<1 the power spectrum does not exist, the process still exhibits long memory in a generalized sense. The power-law shot noise process appears in many applications in engineering and physics. The proposed results can be used to study such processes as well as to synthesize a random process with long-range dependence View full abstract»

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  • Blind RLS based space-time adaptive 2-D RAKE receivers for DS-CDMA communication systems

    Publication Year: 2000 , Page(s): 2145 - 2150
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (212 KB)  

    The goal of a blind two-dimensional (2-D) RAKE receiver for DS-CDMA is to cancel strong multiuser access interference (MUAI) while optimally combining the signal of interest's (SOI) multipath. The weight vector yielding the optimum SINR for bit decisions may be computed as the “largest” generalized eigenvector of a matrix pencil composed of the SOI plus MUAI and MUAI alone space-time correlation matrices. An alternative blind 2-D RAKE receiver is presented based on RLS-type space-time adaptive filtering that offers competitive performance. The applicability of the scheme to the IS-95 uplink is addressed View full abstract»

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  • Two-dimensional FlR filter design using matrix dilation approach

    Publication Year: 2000 , Page(s): 2074 - 2082
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    This paper develops an approach for the optimal design of two-dimensional (2-D) finite impulse response (FIR) filters based on the minimization of a new performance index. The approach gives analytical expressions for the optimal solution and offers a two-parameter family of suboptimal filters. It is shown that the conventional least square solutions is a member of this family. The approach is based on a result in linear algebra, and used in robust control theory, known as the dilation equation. An efficient numerical algorithm for solving the filter design problem using the dilation equation is proposed, and some techniques for choosing the design parameters are discussed. Finally, some examples are shown illustrating the flexibility of the design using the new approach View full abstract»

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  • Adaptive Bayesian multiuser detection for synchronous CDMA with Gaussian and impulsive noise

    Publication Year: 2000 , Page(s): 2013 - 2028
    Cited by:  Papers (42)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB)  

    We consider the problem of simultaneous parameter estimation and data restoration in a synchronous CDMA system in the presence of either additive Gaussian or additive impulsive white noise with unknown parameters. The impulsive noise is modeled by a two-term Gaussian mixture distribution. Bayesian inference of all unknown quantities is made from the superimposed and noisy received signals. The Gibbs sampler (a Markov chain Monte Carlo procedure) is employed to calculate the Bayesian estimates. The basic idea is to generate ergodic random samples from the joint posterior distribution of all unknown and then to average the appropriate samples to obtain the estimates of the unknown quantities. Adaptive Bayesian multiuser detectors based on the Gibbs sampler are derived for both the Gaussian noise synchronous CDMA channel and the impulsive noise synchronous CDMA channel. A salient feature of the proposed adaptive Bayesian multiuser detectors is that they can incorporate the a priori symbol probabilities, and they produce as output the a posteriori symbol probabilities. (That is, they are “soft-input soft-output” algorithms.) Hence, these methods are well suited for iterative processing in a coded system, which allows the adaptive Bayesian multiuser detector to refine its processing based on the information from the decoding stage, and vice versa-a receiver structure termed the adaptive turbo multiuser detector View full abstract»

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  • Decoupled estimation of DOA and angular spread for a spatially distributed source

    Publication Year: 2000 , Page(s): 1872 - 1882
    Cited by:  Papers (68)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    In mobile communications, local scattering in the vicinity of the mobile results in angular spreading as seen from a base station antenna array. In this paper, we consider the problem of estimating the parameters [direction-of-arrival (DOA) and angular spread] of a spatially distributed source, using a uniform linear array (ULA). A two-step procedure enabling decoupling the estimation of DOA from that of the angular spread is proposed. This method combines a covariance matching algorithm with the use of the extended invariance principle (EXIP). More exactly, the first step makes use of an unstructured model for the part of the covariance matrix that depends on the angular spread. Then, the solution is refined by invoking EXIP. Instead of a 2-D search, the proposed scheme requires two successive 1-D searches. Additionally, the DOA estimate is robust to mismodeling the spatial distribution of the scatterers. A statistical analysis is carried out, and a formula for the asymptotic variance of the estimates is derived. Numerical examples illustrate the performance of the method View full abstract»

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  • Hopfield neural network based algorithms for image restoration and reconstruction. II. Performance analysis

    Publication Year: 2000 , Page(s): 2119 - 2131
    Cited by:  Papers (10)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    For pt. I see ibid., vol.48, no.7, p.2105-18 (2000). In this paper, we analyze four typical sequential Hopfield (1982) neural network (HNN) based algorithms for image restoration and reconstruction, which are the modified HNN (PK) algorithm, the HNN (ZCVJ) algorithm with energy checking, the eliminating-highest-error (EHE) algorithm, and the simulated annealing (SA) algorithm. A new measure, the correct transition probability (CTP), is proposed for the performance of iterative algorithms and is used in this analysis. The CTP measures the correct transition probability for a neuron transition at a particular time and reveals the insight of the performance at each iteration. The general properties of the CTP are discussed. Derived are the CTP formulas of these four algorithms. The analysis shows that the EHE algorithm has the highest CTP in all conditions of the severity of blurring, the signal-to-noise ratio (SNR) of a blurred noisy image, and the regularization term. This confirms the result in many previous simulations that the EHE algorithms can converge to more accurate images with much fewer iterations, have much higher correct transition rates than other HNN algorithms, and suppress streaks in restored images. The analysis also shows that the CTPs of all these algorithms decrease with the severity of blurring, the severity of noise, and the degree of regularization, which also confirms the results in previous simulations. This in return suggests that the correct transition probability be a rational performance measure View full abstract»

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  • Prediction-based lower triangular transform

    Publication Year: 2000 , Page(s): 1947 - 1955
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB)  

    A new nonunitary transform called the prediction-based lower triangular transform (PLT) is introduced for signal compression. The new transform has the same decorrelation property as the Kahurnen-Loeve transform (KLT), but its implementational cost is less than one half of KLT. Compared with the KLT, the design cost of an M×M PLT is much lower and is only of the order of O(M2). Moreover, the PLT can be factorized into simple building blocks. Using two different factorizations, we introduce two minimum noise structures that have roughly the same complexity as the direct implementation of PLT. These minimum noise structures have the following properties: (1) its noise gain is unity even though the transform is nonunitary; (2) perfect reconstruction is structurally guaranteed; (3) it can be used for both lossy/lossless compression. We show that the coding gain of PLT implemented using the minimum noise structure is the same as that of KLT. Furthermore, universal transform coders using PLT are derived. For AR(1) process, the M×M PLT has a closed form and needs only (M-1) multiplications and additions View full abstract»

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  • Exponential asymptotic stability of time-varying inverse prediction error filters

    Publication Year: 2000 , Page(s): 1928 - 1936
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (212 KB)  

    It is a classical result of linear prediction theory that as long as the minimum prediction error variance is nonzero, the transfer function of the optimum linear prediction error filter for a stationary process is minimum phase, and therefore, its inverse is exponentially stable. Here, extensions of this result to the case of nonstationary processes are investigated. In that context, the filter becomes time-varying, and the concept of “transfer function” ceases to make sense. Nevertheless, we prove that under mild condition on the input process, the inverse system remains exponentially stable. We also consider filters obtained in a deterministic framework and show that if the time-varying coefficients of the predictor are computed by means of the recursive weighted least squares algorithm, then its inverse remains exponentially stable under a similar set of conditions View full abstract»

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  • Lagrange/Vandermonde MUI eliminating user codes for quasi-synchronous CDMA in unknown multipath

    Publication Year: 2000 , Page(s): 2057 - 2073
    Cited by:  Papers (16)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB)  

    A family of codes for low-complexity quasi-synchronous code division multiple access (CDMA) systems is developed in order to eliminate multiuser interference (MUI) completely in the presence of unknown and even rapidly varying multipath. Judiciously designed precomputable symbol-periodic user codes, which we term Lagrange or Vandermonde, and the corresponding linear receivers offer a generalization of orthogonal frequency division multiplexing (OFDM), which are especially valuable when deep-fading, carrier frequency errors, and Doppler effects are present. The flexibility inherent to the designed transceivers is exploited to derive transmission strategies that cope with major impairments of wireless CDMA channels. The symbol-periodic code design is also generalized to include the class of aperiodic spreading and orthogonal multirate codes for variable bit rate users. Performance analysis and simulations results illustrate the advantages of the proposed scheme over competing alternatives View full abstract»

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  • Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm

    Publication Year: 2000 , Page(s): 1915 - 1927
    Cited by:  Papers (44)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (372 KB)  

    The Levenberg-Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss-Newton algorithms. The advantages and disadvantages of the different algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally the recursive Levenberg-Marquardt algorithm has better convergence properties than the other algorithms View full abstract»

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  • Efficient robust adaptive beamforming for cyclostationary signals

    Publication Year: 2000 , Page(s): 1893 - 1901
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB)  

    This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the LS-SCORE algorithm presented in a paper by Agee et al. (1990) has been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the LS-SCORE algorithm in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. An efficient method is then developed based on the fact that the array output power approaches its maximum as the CFE is reduced. We formulate the problem as an optimization problem for reducing the CFE iteratively to achieve robust adaptive beamforming against the CFE. Simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are provided View full abstract»

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  • MA estimation in polynomial time

    Publication Year: 2000 , Page(s): 1999 - 2012
    Cited by:  Papers (41)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB)  

    The parameter estimation of moving-average (MA) signals from second-order statistics was deemed for a long time to be a difficult nonlinear problem for which no computationally convenient and reliable solution was possible. We show how the problem of MA parameter estimation from sample covariances can be formulated as a semidefinite program that can be solved in a time that is a polynomial function of the MA order. Two methods are proposed that rely on two specific (over) parametrizations of the MA covariance sequence, whose use makes the minimization of a covariance fitting criterion a convex problem. The MA estimation algorithms proposed here are computationally fast, statistically accurate, and reliable. None of the previously available algorithms for MA estimation (methods based on higher-order statistics included) shares all these desirable properties. Our methods can also be used to obtain the optimal least squares approximant of an invalid (estimated) MA spectrum (that takes on negative values at some frequencies), which was another long-standing problem in the signal processing literature awaiting a satisfactory solution View full abstract»

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Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens