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Signal Processing, IEEE Transactions on

Issue 11 • Date Nov. 2000

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Displaying Results 1 - 25 of 31
  • Comments on blind beamforming for multiple non-Gaussian signals and the constant-modulus algorithm

    Page(s): 3248 - 3250
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (108 KB)  

    For original paper see Sheinvald (IEEE Trans. Signal Processing, vol.46, p.1878-85 July 1998). Sheinvald compared a cumulant matching criterion to three simplified criteria that he claimed to be equivalent. We show that two simplified criteria can be viewed as direct consequences of a result already published and that their equivalence must be revised. We also give additional references of closely related works that have been omitted, some of them presenting Jacobi-like source separation algorithms, presented as original by Sheinvald. View full abstract»

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  • Comments on "Self-adaptive source separation. I. Convergence analysis of direct linear network controlled by the Herault-Jutten algorithm" [with reply]

    Page(s): 3255 - 3257
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    In the comment, Chen shows that the main results obtained in the paper by Macchi and Moreau (IEEE Trans. Signal Processing, vol.45, p.918-26, Apr. 1997) have been addressed in Chen and Chen (1994). In a reply Macchi and Moreau discuss the points mentioned by Chen. View full abstract»

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  • Two-dimensional DCT/DST universal computational structure for 2m×2n block sizes

    Page(s): 3250 - 3255
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    A refined generalized signal flow graph for the direct two-dimensional discrete cosine transform (2-D DCT) and discrete sine transform (2-D DST) computation (the so-called 2-D DCT/DST universal computational structure) is described. It represents a generalized unified approach to the fast 2-D DCT and 2-D DST computation for any 2 m×2n block sizes, i.e., both square and rectangular blocks, including the one-dimensional (1-D) case. The regular structure, moderate arithmetic complexity, numerical stability, and multiple block size capability makes it suitable for VLSI or parallel implementation View full abstract»

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  • Convergence analysis of the binormalized data-reusing LMS algorithm

    Page(s): 3235 - 3242
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB)  

    Normalized least mean squares algorithms for FIR adaptive filtering with or without the reuse of past information are known to converge often faster than the conventional least mean squares (LMS) algorithm. This correspondence analyzes an LMS-like algorithm: the binormalized data-reusing least mean squares (BNDR-LMS) algorithm. This algorithm, which corresponds to the affine projection algorithm for the case of two projections, compares favorably with other normalized LMS-like algorithms when the input signal is correlated. Convergence analyses in the mean and in the mean-squared are presented, and a closed-form formula for the mean squared error is provided for white input signals as well as its extension to the case of a colored input signal. A simple model for the input-signal vector that imparts simplicity and tractability to the analysis of second-order statistics is fully described. The methodology is readily applicable to other adaptation algorithms of difficult analysis. Simulation results validate the analysis and ensuing assumptions View full abstract»

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  • A class of subspace tracking algorithms based on approximation of the noise-subspace

    Page(s): 3231 - 3235
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB)  

    This correspondence introduces a novel class of so-called subspace tracking algorithms applicable to, for example, sensor array signal processing. The basic idea pursued in this correspondence is to reduce the amount of computations required for an exact SVD update, applying a perturbation-like strategy, which is interpreted as an approximation of a noise subspace. An interesting property of the derived algorithms is that they can be applied to SVD updating of both auto- and cross-covariance matrices View full abstract»

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  • Multiresolution circular harmonic decomposition

    Page(s): 3242 - 3247
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2108 KB)  

    A dictionary of complex waveforms suited for multiresolution analysis and individually steerable by multiplication by a complex factor is presented. It is based on circular harmonic wavelets (CHW) and is useful for pattern analysis under rotations. The main theoretical aspects of CHWs are illustrated, and an example of application to motion estimation is provided View full abstract»

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  • A new approach to spectral estimation: a tunable high-resolution spectral estimator

    Page(s): 3189 - 3205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB)  

    Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimates. Here, we present a new approach to spectral estimation, which is based on the use of filter banks as a means of obtaining spectral interpolation data. Such data replaces standard covariance estimates. A computational procedure for obtaining suitable pole-zero (ARMA) models from such data is presented. The choice of the zeros (MA-part) of the model is completely arbitrary. By suitable choices of filter bank poles and spectral zeros, the estimator can be tuned to exhibit high resolution in targeted regions of the spectrum View full abstract»

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  • On the existence of efficient estimators

    Page(s): 3028 - 3031
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    The common signal processing problem of estimating some nonrandom parameters of a signal in additive noise is considered. The problem investigated in this paper is under what conditions an efficient estimator exists, i.e., an unbiased estimator with a variance equal to the Cramer-Rao lower bound (CRB). It is well known that if the signal is linear or, more generally, affine in the parameters and the noise Gaussian, an efficient estimator does exist. This paper shows that under some conditions, this is the only case where an efficient estimator exists View full abstract»

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  • Square-root QR inverse iteration for tracking the minor subspace

    Page(s): 2994 - 2999
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB)  

    A new algorithm for tracking the eigenvectors associated with the r smallest eigenvalues of an N×N covariance matrix is introduced. The method is sequential inverse iteration based on a recursive square-root QR factor updating of the covariance matrix with O(N2 r) operations per time update. The principal operations count of this new tracker is justified by a significantly better performance compared with the fast O(Nr2) minor subspace tracker of Douglas et al. (1998) View full abstract»

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  • MIMO system identification: state-space and subspace approximations versus transfer function and instrumental variables

    Page(s): 3087 - 3099
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB)  

    The identification of multi-input multi-output (MIMO) linear systems has previously received a new impetus with the introduction of the state-space (SS) approach based on subspace approximations. This approach has immediately gained popularity, owing to the fact that it avoids the use of canonical forms, requires the determination of only one structural parameter, and has been empirically shown to yield MIMO models with good accuracy in many cases, However, the SS approach suffers from several drawbacks: there is no well-established rule tied to this approach for determining the structural parameter, and, perhaps more important the SS parameter estimates depend on the data in a rather complicated way, which renders almost futile any attempt to analyze and optimize the performance of the estimator. In this paper, we consider a transfer function (TF) approach based on instrumental variables (IV), as an alternative to the SS approach. We use the simplest canonical TF parameterization in which the denominator is equal to a scalar polynomial times the identity matrix. The analysis and optimization of the statistical accuracy of the TF approach is straightforward. Additionally, a simple test tailored to this approach is devised for estimating the single structural parameter needed. A simulation study, in which we compare the performances of the SS and the TF approaches, shows that the latter can provide more accurate models than the former at a lower computational cost View full abstract»

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  • QR-factorization method for computing the greatest common divisor of polynomials with inexact coefficients

    Page(s): 3042 - 3051
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (228 KB)  

    This paper presents a novel means of computing the greatest common divisor (GCD) of two polynomials with real-valued coefficients that have been perturbed by noise. The method involves the QR-factorization of a near-to-Toeplitz matrix derived from the Sylvester matrix of the two polynomials. It turns out that the GCD to within a constant factor is contained in the last nonzero row of the upper triangular matrix R in the QR-factorization of the near-to-Toeplitz matrix. The QR-factorization is efficiently performed by an algorithm due to Chun et al. (1987). A condition number estimator due to Bischof (1990) and an algorithm for rank estimation due to Zarowski (1998) are employed to account for the effects of noise View full abstract»

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  • Linear prediction approach for joint blind equalization and blind multiuser detection in CDMA systems

    Page(s): 3134 - 3145
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (284 KB)  

    For high-speed code division multiple access (CDMA) systems, interchip interference that is caused by multipath propagation becomes severe and cannot be ignored. Together with the multiple access interference due to nonideal conditions in CDMA, they become major obstacles to overall system performance. In this paper, both batch and adaptive algorithms based on linear prediction are proposed for joint blind equalization and multiuser detection for asynchronous CDMA systems. It is shown that the new methods are insensitive to estimation error of propagation delay or chip timing. In addition, the adaptive algorithm is computationally efficient. Simulation results show that the proposed methods are also near-far resistant and compare favorably to many existing methods View full abstract»

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  • Structures, factorizations, and design criteria for oversampled paraunitary filterbanks yielding linear-phase filters

    Page(s): 3062 - 3071
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    We present here a special class of oversampled filterbanks (FBs), namely, paraunitary FBs with linear-phase filters. We propose some necessary conditions for the existence of such banks, based on the repartition between type I/II and type II/IV linear-phase filters in the bank. For a subset of these FBs, we develop a factorization that leads to a minimal implementation, as well as a direct parameterization of the FBs in terms of elementary rotation angles. This factorization is applied to some design examples, with two different optimization criteria: coding gain and reconstructibility of lost coefficients View full abstract»

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  • Order-recursive RLS Laguerre adaptive filtering

    Page(s): 3000 - 3010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    This paper solves the problem of designing recursive-least-squares (RLS) lattice (or order-recursive) algorithms for adaptive filters that do not involve tapped-delay-line structures. In particular, an RLS-Laguerre lattice filter is obtained View full abstract»

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  • α-repetition/modulation and blind second-order identification

    Page(s): 3153 - 3161
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB)  

    In the context of redundant filter-bank precoders for blind second-order equalization, we consider the α-repetition/modulation scheme. Although it is theoretically possible, the identification of a bandlimited communication channel suffers from numerical problems if α is beyond a bound. If α is below this bound, simulation examples illustrate the robustness of the channel estimate View full abstract»

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  • Acoustic vector-sensor processing in the presence of a reflecting boundary

    Page(s): 2981 - 2993
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (260 KB)  

    We consider the passive direction-of-arrival (DOA) estimation problem using arrays of acoustic vector sensors located in a fluid at or near a reflecting boundary. We formulate a general measurement model applicable to any planar surface, derive an expression for the Cramer-Rao bound (CRB) on the azimuth and elevation of a single source, and obtain a bound on the mean-square angular error (MSAE). We then examine two applications of great practical interest: hull-mounted and seabed arrays. For the former, we use three models for the hull: an ideal rigid surface for high frequency, an ideal pressure-release surface for low frequency, and a more complex, realistic layered model. For the seabed scenario, we model the ocean floor as an absorptive liquid layer. For each application, we use the CRB, MSAE bound, and beam patterns to quantify the advantages of using velocity and/or vector sensors instead of pressure sensors. For the hull-mounted application, we show that normal component velocity sensors overcome the well-known, low-frequency problem of small pressure signals without the need for an undesirable “stand-off” distance. For the seabed scenario, we also derive a fast wideband estimator of the source location using a single vector sensor View full abstract»

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  • MUI-free receiver for a synchronous DS-CDMA system based on block spreading in the presence of frequency-selective fading

    Page(s): 3175 - 3188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB)  

    We discuss a synchronous direct-sequence code division multiple-access (DS-CDMA) system based on block spreading in the presence of frequency-selective fading. Note that block spreading, which is also known as chip interleaving, refers to a spreading of a data block sequence, which is obtained by dividing a data symbol sequence into consecutive blocks. For such a system, we develop a simple new receiver that completely removes the multiuser interference (MUI) without using any channel information. The MUI-free operation is obtained by the use of a shift-orthogonal set of code sequences on which this receiver is based. Within the framework of the MUI-free receiver, we further present a subspace deterministic blind single-user channel estimation algorithm. As a benchmark for the MUI-free receiver and the corresponding subspace deterministic blind single-user channel estimation algorithm, we consider the linear multiuser equalizer and the corresponding subspace deterministic blind multiuser channel estimation algorithm developed by Liu and Xu (1996) for a standard synchronous DS-CDMA system in the presence of frequency-selective fading. We show that the complexity of the MUI-free receiver using the corresponding subspace deterministic blind single-user channel estimation algorithm is much smaller than the complexity of the linear multiuser equalizer using the corresponding subspace deterministic blind multiuser channel estimation algorithm. We further show that the performance of the MUI-free receiver is comparable with the performance of the linear multiuser equalizer. This is for the case in which the channels are known as well as for the case in which the channels are estimated with the corresponding subspace deterministic blind channel estimation algorithm View full abstract»

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  • Support vector machine techniques for nonlinear equalization

    Page(s): 3217 - 3226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (788 KB)  

    The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem View full abstract»

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  • Efficient VLSI architectures for fast computation of the discrete Fourier transform and its inverse

    Page(s): 3206 - 3216
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (960 KB)  

    In this paper, we propose two new VLSI architectures for computing the N-point discrete Fourier transform (DFT) and its inverse (IDFT) based on a radix-2 fast algorithm, where N is a power of two. The first part of this work presents a linear systolic array that requires log2 N complex multipliers and is able to provide a throughput of one transform sample per clock cycle. Compared with other related systolic designs based on direct computation or a radix-2 fast algorithm, the proposed one has the same throughput performance but involves less hardware complexity. This design is suitable for high-speed real-time applications, but it would not be easily realized in a single chip when N gets large. To balance the chip area and the processing speed, we further present a new reduced-complexity design for the DFT/IDFT computation. The alternative design is a memory-based architecture that consists of one complex multiplier, two complex adders, and some special memory units. The new design has the capability of computing one transform sample every log2 N+1 clock cycles on average. In comparison with the first design, the second design reaches a lower throughput with less hardware complexity. As N=512, the chip area required for the memory-based design is about 5742×5222 μm2, and the corresponding throughput can attain a rate as high as 4M transform samples per second under 0.6 μm CMOS technology. Such area-time performance makes this design very competitive for use in long-length DFT applications, such as asymmetric digital subscriber lines (ADSL) and orthogonal frequency-division multiplexing (OFDM) systems View full abstract»

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  • Blind identification of an autoregressive system using a nonlinear dynamical approach

    Page(s): 3017 - 3027
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (272 KB)  

    The problem of identifying an autoregressive (AR) system with arbitrary driven noise is considered here. Using an abstract dynamical system to represent both chaotic and stochastic processes in a unified framework, a dynamic-based complexity measure called phase space volume (PSV), which has its origins in chaos theory, can be applied to identify an AR model in chaotic as well as stochastic noise environments. It is shown that the PSV of the output signal of an inverse filter applied to identify an AR model is always larger than the PSV of the input signal of the AR model. Therefore, by minimizing the PSV of the inverse filter output, one can estimate the coefficients and the order of the AR system. A major advantage of this minimum-phase space volume (MPSV) identification technique is that it works like a universal estimator that does not require precise statistical information about the AR input signal. Because the theoretical PSV is so difficult to compute, two approximations of PSV are also considered: the e-PSV and nearest neighbor PSV. Both approximations are shown to approach the ideal PSV asymptotically. The identification performance based on these two approximations are evaluated using Monte Carlo simulations. Both approximations are found to generate relatively good results in identifying an AR system in various noise environments, including chaotic, non-Gaussian, and colored noise View full abstract»

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  • Blind digital signal separation using successive interference cancellation iterative least squares

    Page(s): 3146 - 3152
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    Blind separation of instantaneous linear mixtures of digital signals is a basic problem in communications. When little or nothing can be assumed about the mixing matrix, signal separation may be achieved by exploiting structural properties of the transmitted signals, e.g., finite alphabet or coding constraints. We propose a monotonically convergent and computationally efficient iterative least squares (ILS) blind separation algorithm based on an optimal scaling lemma. The signal estimation step of the proposed algorithm is reminiscent of successive interference cancellation (SIC) ideas. For well-conditioned data and moderate SNR, the proposed SIC-ILS algorithm provides a better performance/complexity tradeoff than competing ILS algorithms. Coupled with blind algebraic digital signal separation methods, SIC-ILS offers a computationally inexpensive true least squares refinement option. We also point out that a widely used ILS finite alphabet blind separation algorithm can exhibit limit cycle behavior View full abstract»

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  • Rational invariant subspace approximations with applications

    Page(s): 3032 - 3041
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB)  

    Subspace methods such as MUSIC, minimum norm, and ESPRIT have gained considerable attention due to their superior performance in sinusoidal and direction-of-arrival (DOA) estimation, but they are also known to be of high computational cost. In this paper, new fast algorithms for approximating signal and noise subspaces and that do not require exact eigendecomposition are presented. These algorithms approximate the required subspace using rational and power-like methods applied to the direct data or the sample covariance matrix. Several ESPRIT- as well as MUSIC-type methods are developed based on these approximations. A substantial computational saving can be gained comparing with those associated with the eigendecomposition-based methods. These methods are demonstrated to have performance comparable to that of MUSIC yet will require fewer computations to obtain the signal subspace matrix View full abstract»

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  • Cosine-modulated filterbanks based on extended Gaussian functions

    Page(s): 3052 - 3061
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (328 KB)  

    A new family of cosine-modulated filterbanks based on functions called extended Gaussian functions (EGFs) is obtained. The design is particularly simple since it is mainly based on a closed-form expression. Nearly perfect reconstruction cosine-modulated filterbanks are obtained as well as guidelines to estimate the filterbank parameters. This analytical design method can be used to produce, with a controlled accuracy, filterbanks with practically no upper limitations in the number of coefficients and subbands. Furthermore, a slight modification of the prototype filter coefficients is sufficient to satisfy exactly the perfect reconstruction constraints. An analysis of the time-frequency localization of the discrete prototype filters also shows that under certain conditions, EGF prototypes are at less than 0.3% from the optimal upper bound View full abstract»

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  • Alpha-stable signals and adaptive filtering

    Page(s): 3011 - 3016
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (176 KB)  

    Let {Dk,Xk} be jointly stationary symmetric α-stable processes with index 1<α<2. We first consider the problem of optimal FIR linear and regression filters. We then consider the signed-error and sign-sign adaptive filtering algorithms for the estimation of the desired signal Dj on the basis of the input vector Xj. Various convergence results are established for the signal and tap weights estimation errors 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
Zhi-Quan (Tom) Luo
University of Minnesota