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Signal Processing, IEEE Transactions on

Issue 10 • Date Oct. 1999

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Displaying Results 1 - 25 of 30
  • Abstracts of manuscripts in review

    Publication Year: 1999 , Page(s): 2900 - 2901
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  • Universal linear prediction by model order weighting

    Publication Year: 1999 , Page(s): 2685 - 2699
    Cited by:  Papers (44)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    A common problem that arises in adaptive filtering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error for any bounded individual sequence is as good as the performance attainable by the best sequential linear predictor of order less than some M. This predictor is found by transforming linear prediction into a problem analogous to the sequential probability assignment problem from universal coding theory. The resulting universal predictor uses essentially a performance-weighted average of all predictors for model orders less than M. Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order. Examples of prediction performance are provided for autoregressive and speech data as well as an example of adaptive data equalization View full abstract»

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  • Source separation in post-nonlinear mixtures

    Publication Year: 1999 , Page(s): 2807 - 2820
    Cited by:  Papers (121)
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    We address the problem of separation of mutually independent sources in nonlinear mixtures. First, we propose theoretical results and prove that in the general case, it is not possible to separate the sources without nonlinear distortion. Therefore, we focus our work on specific nonlinear mixtures known as post-nonlinear mixtures. These mixtures constituted by a linear instantaneous mixture (linear memoryless channel) followed by an unknown and invertible memoryless nonlinear distortion, are realistic models in many situations and emphasize interesting properties i.e., in such nonlinear mixtures, sources can be estimated with the same indeterminacies as in instantaneous linear mixtures. The separation structure of nonlinear mixtures is a two-stage system, namely, a nonlinear stage followed by a linear stage, the parameters of which are updated to minimize an output independence criterion expressed as a mutual information criterion. The minimization of this criterion requires knowledge or estimation of source densities or of their log-derivatives. A first algorithm based on a Gram-Charlier expansion of densities is proposed. Unfortunately, it fails for hard nonlinear mixtures. A second algorithm based on an adaptive estimation of the log-derivative of densities leads to very good performance, even with hard nonlinearities. Experiments are proposed to illustrate these results View full abstract»

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  • Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm

    Publication Year: 1999 , Page(s): 2655 - 2666
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB)  

    We propose a maximum likelihood (ML) approach for tracking the direction-of-arrival (DOA) of multiple moving targets by a passive array. A locally linear model is assumed for the target motion, and the multiple target states (MTSs) are defined to describe the states of the target motion, The locally linear model is shown to be strongly locally observable almost everywhere. The approach is to estimate the initial MTS by maximizing the likelihood function of the array data. The tracking is implemented by prediction through the target motion dynamics using the initial MTS estimate. By incorporating the target motion dynamics, the algorithm is able to eliminate the spread spectrum effects due to target motion. A modified Newton-type algorithm is also presented, which ensures fast convergence of the algorithm. Finally, numerical simulations are included to show the effectiveness of the proposed algorithm View full abstract»

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  • Fractional Fourier series expansion for finite signals and dual extension to discrete-time fractional Fourier transform

    Publication Year: 1999 , Page(s): 2883 - 2888
    Cited by:  Papers (30)
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    Conventional Fourier analysis has many schemes for different types of signals. They are Fourier transform (FT), Fourier series (FS), discrete-time Fourier transform (DTFT), and discrete Fourier transform (DFT). The goal of this article is to develop two absent schemes of fractional Fourier analysis methods. The proposed methods are fractional Fourier series (FRFS) and discrete-time fractional Fourier transform (DTFRFT), and they are the generalizations of Fourier series (FS) and discrete-time Fourier transform (DTFT), respectively View full abstract»

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  • Critic-driven ensemble classification

    Publication Year: 1999 , Page(s): 2833 - 2844
    Cited by:  Papers (9)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (276 KB)  

    We develop new rules for combining the estimates obtained from each classifier in an ensemble, in order to address problems involving multiple (>2) classes. A variety of techniques have been previously suggested, including averaging probability estimates from each classifier, as well as hard (0-1) voting schemes. In this work, we introduce the notion of a critic associated with each classifier, whose objective is to predict the classifier's errors. Since the critic only tackles a two class problem, its predictions are generally more reliable than those of the classifier and, thus, can be used as the basis for improved combination rules. Several such rules are suggested here. While previous techniques are only effective when the individual classifier error rate is p<0.5, the new approach is successful, as proved under an independence assumption, even when this condition is violated-in particular, so long as p+q<1, with q the critic's error rate. More generally, critic-driven combining is found to achieve significant performance gains over alternative methods on a number of benchmark data sets. We also propose a new analytical tool for modeling ensemble performance, based on dependence between experts. This approach is substantially more accurate than the analysis based on independence that is often used to justify ensemble methods View full abstract»

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  • Detection and estimation of multiplexed soliton signals

    Publication Year: 1999 , Page(s): 2768 - 2782
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (296 KB)  

    Solitons are eigenfunction solutions to certain nonlinear wave equations that arise in a variety of natural and man-made systems. Their rich properties and tractability make them an intriguing component of such systems, often describing the large-scale or long-term behavior of natural systems, or the information content in certain communication or signal processing systems. However, it is often difficult to detect or estimate the parameters of solitons in such systems due to the presence of strong nonsoliton components or the nonlinear interaction of multiple solitons. The objective of this paper is to develop and investigate the detection and estimation of soliton signals. As a framework for this study, we consider using these nonlinear systems as both signal generators and signal processors in a form of multiplexed soliton communication. In contrast to more conventional uses of solitons in a communications context, our communication system uses soliton systems for signal generation and multiplexing for transmission over traditional linear channels. In addition to their mathematical tractability and the simplicity of the analog circuits used to generate and process them, we show that the soliton signal dynamics may also provide a mechanism for decreasing transmitted signal energy while enhancing signal detection and parameter estimation performance View full abstract»

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  • Resolving manifold ambiguities in direction-of-arrival estimation for nonuniform linear antenna arrays

    Publication Year: 1999 , Page(s): 2629 - 2643
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB)  

    This paper addresses the problem of ambiguities in direction of arrival (DOA) estimation for nonuniform (sparse) linear arrays. Usually, DOA estimation ambiguities are associated with linear dependence among the points on the antenna array manifold, that is, the steering vectors degenerate so that each may be expressed as a linear combination of the others. Most nonuniform array geometries, including the so-called “minimum redundancy” arrays, admit such manifold ambiguities. While the standard subspace algorithms such as MUSIC fail to provide unambiguous DOA estimates under these conditions, we demonstrate that this failure does not necessarily imply that consistent and asymptotically effective DOA estimates do not exist. We demonstrate that in most cases involving uncorrelated Gaussian sources, manifold ambiguity does not necessarily imply nonidentifiability; most importantly, we introduce algorithms designed to resolve manifold ambiguity. We also show that for situations where the number of sources exceeds the number of array sensors, a new class of locally nonidentifiable scenario exists View full abstract»

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  • Linear phase paraunitary filter bank with filters of different lengths and its application in image compression

    Publication Year: 1999 , Page(s): 2730 - 2744
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1120 KB)  

    In this paper, the theory, structure, design, and implementation of a new class of linear-phase paraunitary filter banks (LPPUFBs) are investigated. The novel filter banks with filters of different lengths can be viewed as the generalized lapped orthogonal transforms (GenLOTs) with variable-length basis functions. Our main motivation is the application in block-transform-based image coding. Besides having all of the attractive properties of other lapped orthogonal transforms, the new transform takes advantage of its long, overlapping basis functions to represent smooth signals in order to reduce blocking artifacts, whereas it reserves short basis functions for high-frequency signal components like edges and texture, thereby limiting ringing artifacts. Two design methods are presented, each with its own set of advantages: the first is based on a direct lattice factorization, and the second enforces certain relationships between the lattice coefficients to obtain variable length filters. Various necessary conditions for the existence of meaningful solutions are derived and discussed in both cases. Finally, several design and image coding examples are presented to confirm the validity of the theory View full abstract»

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  • Dynamics of multichannel feedforward adaptive systems

    Publication Year: 1999 , Page(s): 2700 - 2709
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    The role played by reference-signal compensation in the convergence of multichannel feedforward adaptive systems is addressed. Convergence conditions are established, and effects of compensation errors are analyzed. We also explore, by means of altering the reference-signal compensation filtering from the conventional choice, ways to force systems to converge to arbitrary solutions of possible interest other than the standard Wiener solution. This could be useful, for example, if one wished to cancel noise at a very large number of measurement points, which, conventionally, would require a correspondingly complex, possibly prohibitively large, controller. The scheme developed enables very efficient usage of error signals, such that systems with large numbers of disturbance-cancellation points need employ only a relatively small number of error signals in the actual control-system implementation View full abstract»

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  • Quantitative Fourier analysis of approximation techniques. I. Interpolators and projectors

    Publication Year: 1999 , Page(s): 2783 - 2795
    Cited by:  Papers (74)  |  Patents (1)
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    We present a general Fourier-based method that provides an accurate prediction of the approximation error as a function of the sampling step T. Our formalism applies to an extended class of convolution-based signal approximation techniques, which includes interpolation, generalized sampling with prefiltering, and the projectors encountered in wavelet theory. We claim that we can predict the L2-approximation error by integrating the spectrum of the function to approximate-not necessarily bandlimited-against a frequency kernel E(ω) that characterizes the approximation operator. This prediction is easier yet more precise than was previously available. Our approach has the remarkable property of providing a global error estimate that is the average of the true approximation error over all possible shifts of the input function. Our error prediction is exact for stationary processes, as well as for bandlimited signals. We apply this method to the comparison of standard interpolation and approximation techniques. Our method has interesting implications for approximation theory. In particular, we use our results to obtain some new asymptotic expansions of the error as T→0, as well as to derive improved upper bounds of the kind found in the Strang-Fix (1971) theory. We finally show how we can design quasi-interpolators that are near optimal in the least-squares sense View full abstract»

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  • Cross-product algorithms for source tracking using an EM vector sensor

    Publication Year: 1999 , Page(s): 2863 - 2867
    Cited by:  Papers (21)
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    We present an adaptive cross-product algorithm for tracking the direction to a moving source using an electromagnetic vector sensor and analyze its performance. We then propose a multiple forgetting factor variant of the same algorithm, which has self-tuning capability, numerical examples are included View full abstract»

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  • Stack filters, stack smoothers, and mirrored threshold decomposition

    Publication Year: 1999 , Page(s): 2757 - 2767
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (292 KB)  

    Stack smoothers have received considerable attention in signal processing in the past decade. Stack smoothers define a large class of nonlinear smoothers based on positive Boolean functions (PBF) applied in the binary domain of threshold decomposition. Although stack smoothers can offer some advantages over traditional linear FIR filters, they are in essence smoothers lacking the flexibility to adequately address a number of signal processing problems that require bandpass or highpass filtering characteristics. In this paper, mirrored threshold decomposition is introduced, which, together with the associated binary PBF, define the significantly richer class of stack filters. Using threshold logic representation, a number of properties of stack filters are derived. Notably, stack filters defined in the binary domain of mirrored threshold decomposition require the use of double weighting of each sample in the integer domain. The class of recursive stack filters and the corresponding recursive weighted median (RWM) filters in the integer domain admitting negative weights are introduced. The new stack filter formulation leads to a more powerful class of estimators capable of effectively addressing a number of fundamental problems in signal processing that could not adequately be addressed by prior stack smoother structures View full abstract»

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  • Dynamic algorithm transforms for low-power reconfigurable adaptive equalizers

    Publication Year: 1999 , Page(s): 2821 - 2832
    Cited by:  Papers (10)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB)  

    In this paper, we present low-power reconfigurable adaptive equalizers derived via dynamic algorithm transforms (DATs). The principle behind DAT is that conventional signal processing systems are designed for the worst case and are not energy-optimum on average. Therefore, significant energy savings can be achieved by optimally reconfiguring the hardware in these situations. Practical reconfiguration strategies for adaptive filters are presented. These strategies are derived as a solution to an optimization problem. The optimization problem has energy as the objective function and a constraint on the algorithm performance (specifically the SNR). The DAT-based adaptive filter is employed as an equalizer for a 51.84 Mb/s very high speed digital subscriber loop (VDSL) over 24-pair BKMA cable. The channel nonstationarities are due to variations in cable length and number of far end crosstalk (FEXT) interferers. For this application, the traditional design is based on 1 kft cable length and 11 FEXT interferers. It was found that up to 81% energy savings can be achieved when cable length varies from 1-0.1 kft and the number of FEXT interferers varies from 11 to 4. On the average, 53% energy savings are achieved as compared with the conventional worst-case design View full abstract»

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  • Convergence of an iterative time-variant filtering based on discrete Gabor transform

    Publication Year: 1999 , Page(s): 2894 - 2899
    Cited by:  Papers (9)
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    An iterative time-variant filtering based on the discrete Gabor transform (DGT) has been previously proposed by the authors. In this article, we present a proof of the convergence of the iterative algorithm under a sufficient condition on the analysis and synthesis window functions of the DGT. In the meantime, we show that the iterative algorithm refines the least squares solution View full abstract»

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  • Tracking the direction-of-arrival of multiple moving targets by passive arrays: asymptotic performance analysis

    Publication Year: 1999 , Page(s): 2644 - 2654
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (340 KB)  

    In the companion paper of Zhou, Yip and Leung (see ibid., vol.47, no.10, p.2655-66, 1999), the maximum likelihood (ML) algorithm for tracking the DOAs of multiple moving targets by passive arrays is presented. In this paper, we provide an asymptotic performance analysis of the algorithm. The statistical consistency of the ML estimates is discussed, and their asymptotic covariances are derived. The Cramer-Rao bounds for the ML estimates are investigated, and their relative efficiency conditions are discussed. The asymptotic performance of the ML tracking algorithm is compared with that of the extended Kalman filter (EKF) under the assumption that the target waveforms are known. Finally, numerical simulation results are used to verify the theoretical results View full abstract»

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  • Quantitative Fourier analysis of approximation techniques. II. Wavelets

    Publication Year: 1999 , Page(s): 2796 - 2806
    Cited by:  Papers (25)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (288 KB)  

    For pt.I see ibid., vol.47, no.10, p.2783-95 (1999). In a previous paper, we proposed a general Fourier method that provides an accurate prediction of the approximation error, irrespective of the scaling properties of the approximating functions. Here, we apply our results when these functions satisfy the usual two-scale relation encountered in dyadic multiresolution analysis. As a consequence of this additional constraint, the quantities introduced in our previous paper can be computed explicitly as a function of the refinement filter. This is, in particular, true for the asymptotic expansion of the approximation error for biorthonormal wavelets as the scale tends to zero. One of the contributions of this paper is the computation of sharp, asymptotically optimal upper bounds for the least-squares approximation error. Another contribution is the application of these results to B-splines and Daubechies (1988, 1992) scaling functions, which yields explicit asymptotic developments and upper bounds. Thanks to these explicit expressions, we can quantify the improvement that can be obtained by using B-splines instead of Daubechies wavelets. In other words, we can use a coarser spline sampling and achieve the same reconstruction accuracy as Daubechies. Specifically, we show that this sampling gain converges to π as the order tends to infinity View full abstract»

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  • Extending the threshold and frequency range for phase-based frequency estimation

    Publication Year: 1999 , Page(s): 2857 - 2863
    Cited by:  Papers (28)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB)  

    Standard phase-based frequency estimation has a threshold that is high, is frequency dependent, and does not decrease with increasing signal length. These problems are solved by processing the signal with a highly overlapped filter bank before applying phase-based frequency estimation. By exploiting decimation, a closed-form matrix inversion, and cascades of simple filter banks, the computational complexity is kept low View full abstract»

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  • On wavelet denoising and its applications to time delay estimation

    Publication Year: 1999 , Page(s): 2879 - 2882
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (128 KB)  

    The application of dyadic wavelet decomposition in the context of time delay estimation is investigated. We consider a model in which the source signal is deterministic and the received sensor outputs are corrupted by additive noises. Wavelet denoising is exploited to provide an effective solution for the problem. Denoising is first applied to preprocess the received signals from two spatially separated sensors with an attempt to remove the contamination, and the peak of their cross correlation function is then located from which the time delay between the two signals can be derived. A novel wavelet shrinkage/thresholding technique for denoising is introduced, and the performance of the algorithm is analyzed rigorously. It is proved that the proposed method achieves global convergence with a high probability. Simulation results also corroborate that the technique is efficient and performs significantly better than both the generalized cross correlator (GCC) and the direct cross correlator (CC) View full abstract»

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  • Estimating directions of arrival of completely and incompletely polarized signals with electromagnetic vector sensors

    Publication Year: 1999 , Page(s): 2845 - 2852
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB)  

    We are concerned with direction-of-arrival estimation and signal classification with electromagnetic vector sensors for scenarios where completely and incompletely polarized signals may co-exist. We propose an efficient ESPRIT-based method, address the identifiability of the proposed method, and compare its performance against CRB View full abstract»

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  • SVD compression, unitary transforms, and computational complexity

    Publication Year: 1999 , Page(s): 2724 - 2729
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (176 KB)  

    The search for fast unitary transforms and the need for data compression in linear systems are complementary issues. Compression requires the definition of a threshold dependent on the condition number, which is invariant over the unitary group. With respect to this threshold, it is shown that the SVD is the optimal tool. Considerations in connection with the Kronecker product and direct sum of unitary matrices show that the computational complexity of unitary transforms is entropy-like in nature, thereby indicating that the O(n log n) complexity unitary transforms are dense over the unitary group View full abstract»

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  • Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC

    Publication Year: 1999 , Page(s): 2667 - 2676
    Cited by:  Papers (80)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (292 KB)  

    In this paper, the problem of joint Bayesian model selection and parameter estimation for sinusoids in white Gaussian noise is addressed. An original Bayesian model is proposed that allows us to define a posterior distribution on the parameter space. All Bayesian inference is then based on this distribution. Unfortunately, a direct evaluation of this distribution and of its features, including posterior model probabilities, requires evaluation of some complicated high-dimensional integrals. We develop an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo methods to perform the Bayesian computation. A convergence result for this algorithm is established. In simulation, it appears that the performance of detection based on posterior model probabilities outperforms conventional detection schemes View full abstract»

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  • A quantitative SNR analysis for the pseudo Wigner-Ville distribution

    Publication Year: 1999 , Page(s): 2891 - 2894
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (116 KB)  

    A quantitative analysis for short-time Fourier transform has been previously given. We analyze the SNR for the pseudo Wigner-Ville distribution with rectangular windows for multicomponent signals in additive noise. The SNR for the pseudo Wigner-Ville distribution in terms of a sampling rate is given for a fixed window length View full abstract»

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  • Adaptive multistage beamforming using cyclic higher order statistics (CHOS)

    Publication Year: 1999 , Page(s): 2867 - 2873
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    A new blind beamforming algorithm for signals that exhibit higher order cyclostationarity is presented. Exploiting some previous theoretical developments, we show how cyclic cumulants of the received signals can be used to obtain the weights of the beamformer that perform blind extraction. The method is based on a spatial interpretation of a deconvolution procedure known as the super-exponential algorithm. The basic block processing algorithm is made fully adaptive using an adaptive URV scheme and applied to a typical mobile communications scenario where several cochannel interferers corrupt the signals of interest View full abstract»

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  • Analysis of adaptive filters using normalized signed regressor LMS algorithm

    Publication Year: 1999 , Page(s): 2710 - 2723
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB)  

    In this paper, adaptive filters using the normalized signed regressor LMS algorithm (NSRA) with Gaussian reference inputs are proposed and analyzed to yield difference equations for theoretically calculating expected convergence of the filters. A simple difference equation for mean squared error (MSE) is derived when the filter input is a white and Gaussian process, whereas approximate difference equations for colored Gaussian inputs are proposed and tested. Stability conditions and residual MSE after convergence are also obtained. Agreement of theoretical results with those of simulation in the experiment with some examples of filter convergence shows sufficient accuracy of the theory and assures the usefulness of the difference equations in estimating filter performances, thus facilitating the design of adaptive filters using the NSRA 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