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Signal Processing, IEEE Transactions on

Issue 5 • Date May 2005

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  • Table of contents

    Publication Year: 2005 , Page(s): c1 - c4
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  • IEEE Transactions on Signal Processing publication information

    Publication Year: 2005 , Page(s): c2
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  • Statistical resolution limits and the complexified Crame´r-Rao bound

    Publication Year: 2005 , Page(s): 1597 - 1609
    Cited by:  Papers (68)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    Array resolution limits and accuracy bounds on the multitude of signal parameters (e.g., azimuth, elevation, Doppler, range, cross-range, depth, frequency, chirp, polarization, amplitude, phase, etc.) estimated by array processing algorithms are essential tools in the evaluation of system performance. The case in which the complex amplitudes of the signals are unknown is of particular practical interest. A computationally efficient formulation of these bounds (from the perspective of derivations and analysis) is presented for the case of deterministic and unknown signal amplitudes. A new derivation is given using the unknown complex signal parameters and their complex conjugates. The new formula is readily applicable to obtaining either symbolic or numerical solutions to estimation bounds for a very wide class of problems encountered in adaptive sensor array processing. This formula is shown to yield several of the standard Crame´r-Rao results for array processing, along with new results of fundamental interest. Specifically, a new closed-form expression for the statistical resolution limit of an aperture for any asymptotically unbiased superresolution algorithm (e.g., MUSIC, ESPRIT) is provided. The statistical resolution limit is defined as the source separation that equals its own Crame´r-Rao bound, providing an algorithm-independent bound on the resolution of any high-resolution method. It is shown that the statistical resolution limit of an array or coherent integration window is about 1.2·SNR-14/ relative to the Fourier resolution limit of 2π/N radians (large number N of array elements). That is, the highest achievable resolution is proportional to the reciprocal of the fourth root of the signal-to-noise ratio (SNR), in contrast to the square-root (SNR-12/) dependence of standard accuracy bounds. These theoretical results are consistent with previously published bounds for specific superresolution algorithms derived by other methods. It is also shown that the potential resolution improvement obtained by separating two collinear arrays (synthetic ultra-wideband), each with a fixed aperture B wavelengths by M wavelengths (assumed large), is approximately (M/B)12/, in contrast to the resolution improv- ement of M/B for a full aperture. Exact closed-form results for these problems with their asymptotic approximations are presented. View full abstract»

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  • Covariance, subspace, and intrinsic Crame´r-Rao bounds

    Publication Year: 2005 , Page(s): 1610 - 1630
    Cited by:  Papers (45)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (896 KB) |  | HTML iconHTML  

    Crame´r-Rao bounds on estimation accuracy are established for estimation problems on arbitrary manifolds in which no set of intrinsic coordinates exists. The frequently encountered examples of estimating either an unknown subspace or a covariance matrix are examined in detail. The set of subspaces, called the Grassmann manifold, and the set of covariance (positive-definite Hermitian) matrices have no fixed coordinate system associated with them and do not possess a vector space structure, both of which are required for deriving classical Crame´r-Rao bounds. Intrinsic versions of the Crame´r-Rao bound on manifolds utilizing an arbitrary affine connection with arbitrary geodesics are derived for both biased and unbiased estimators. In the example of covariance matrix estimation, closed-form expressions for both the intrinsic and flat bounds are derived and compared with the root-mean-square error (RMSE) of the sample covariance matrix (SCM) estimator for varying sample support K. The accuracy bound on unbiased covariance matrix estimators is shown to be about (10/log 10)n/K12/ dB, where n is the matrix order. Remarkably, it is shown that from an intrinsic perspective, the SCM is a biased and inefficient estimator and that the bias term reveals the dependency of estimation accuracy on sample support observed in theory and practice. The RMSE of the standard method of estimating subspaces using the singular value decomposition (SVD) is compared with the intrinsic subspace Crame´r-Rao bound derived in closed form by varying both the signal-to-noise ratio (SNR) of the unknown p-dimensional subspace and the sample support. In the simplest case, the Crame´r-Rao bound on subspace estimation accuracy is shown to be about (p(n-p))12-1/2/SNR-12/ rad for p-dimensional subspaces. It is seen that the SVD-based method yields accuracies very close to the Crame´r-Rao bound, establishing that the principal invariant subspace of a random sample provides an excellent estimator of an unknown subspace. The analysis approach developed is directly applicable to many other estimation problems on manifolds encountered in signal processing and elsewhere, such as estimating rotation matrices in computer vision a- nd estimating subspace basis vectors in blind source separation. View full abstract»

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  • Optimal dimensionality reduction of sensor data in multisensor estimation fusion

    Publication Year: 2005 , Page(s): 1631 - 1639
    Cited by:  Papers (51)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    When there exists the limitation of communication bandwidth between sensors and a fusion center, one needs to optimally precompress sensor outputs-sensor observations or estimates before the sensors' transmission in order to obtain a constrained optimal estimation at the fusion center in terms of the linear minimum error variance criterion, or when an allowed performance loss constraint exists, one needs to design the minimum dimension of sensor data. This paper will answer the above questions by using the matrix decomposition, pseudo-inverse, and eigenvalue techniques. View full abstract»

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  • Fourth-order blind identification of underdetermined mixtures of sources (FOBIUM)

    Publication Year: 2005 , Page(s): 1640 - 1653
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (616 KB) |  | HTML iconHTML  

    For about two decades, numerous methods have been developed to blindly identify overdetermined (P≤N) mixtures of P statistically independent narrowband (NB) sources received by an array of N sensors. These methods exploit the information contained in the second-order (SO), the fourth-order (FO) or both the SO and FO statistics of the data. However, in practical situations, the probability of receiving more sources than sensors increases with the reception bandwidth and the use of blind identification (BI) methods able to process underdetermined mixtures of sources, for which P>N may be required. Although such methods have been developed over the past few years, they all present serious limitations in practical situations related to the radiocommunications context. For this reason, the purpose of this paper is to propose a new attractive BI method, exploiting the information contained in the FO data statistics only, that is able to process underdetermined mixtures of sources without the main limitations of the existing methods, provided that the sources have different trispectrum and nonzero kurtosis with the same sign. A new performance criterion that is able to quantify the identification quality of a given source and allowing the quantitative comparison of two BI methods for each source, is also proposed in the paper. Finally, an application of the proposed method is presented through the introduction of a powerful direction-finding method built from the blindly identified mixture matrix. View full abstract»

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  • Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources

    Publication Year: 2005 , Page(s): 1654 - 1669
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1512 KB) |  | HTML iconHTML  

    A new approach for convolutive blind source separation (BSS) by explicitly exploiting the second-order nonstationarity of signals and operating in the frequency domain is proposed. The algorithm accommodates a penalty function within the cross-power spectrum-based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization. This leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient-based descent algorithm. Using this approach, not only can the degenerate solution induced by a unmixing matrix and the effect of large errors within the elements of covariance matrices at low-frequency bins be automatically removed, but in addition, a unifying view to joint diagonalization with unitary or nonunitary constraint is provided. Numerical experiments are presented to verify the performance of the new method, which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals, in particular, in terms of shape preservation and amplitude ambiguity reduction, as compared with the conventional second-order based algorithms for convolutive mixtures that exploit signal nonstationarity. View full abstract»

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  • An empirical Bayes estimator for in-scale adaptive filtering

    Publication Year: 2005 , Page(s): 1670 - 1683
    Cited by:  Papers (1)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB) |  | HTML iconHTML  

    A scale-adaptive filtering scheme is developed for underspread channels based on a model of the linear time-varying channel operator as a process in scale. Recursions serve the purpose of adding detail to the filter estimate until a suitable measure of fidelity and complexity is met. Resolution of the channel impulse response associated with its coherence time is naturally modeled over the observation time via a Gaussian mixture assignment on wavelet coefficients. Maximum likelihood, approximate maximum a posteriori (MAP) and posterior mean estimators, as well as associated variances, are derived. Doppler spread estimation associated with the coherence time of the filter is synonymous with model order selection and a MAP estimate is presented and compared with Laplace's approximation and the popular AIC. The algorithm is implemented with conjugate-gradient iterations at each scale, and as the coherence time is recursively decreased, the lower scale estimate serves as a starting point for successive reduced-coherence time estimates. The algorithm is applied to a set of simulated sparse multipath Doppler spread channels, demonstrating the superior MSE performance of the posterior mean filter estimator and the superiority of the MAP Doppler spread stopping rule. View full abstract»

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  • Robust minimum variance beamforming

    Publication Year: 2005 , Page(s): 1684 - 1696
    Cited by:  Papers (209)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB) |  | HTML iconHTML  

    This paper introduces an extension of minimum variance beamforming that explicitly takes into account variation or uncertainty in the array response. Sources of this uncertainty include imprecise knowledge of the angle of arrival and uncertainty in the array manifold. In our method, uncertainty in the array manifold is explicitly modeled via an ellipsoid that gives the possible values of the array for a particular look direction. We choose weights that minimize the total weighted power output of the array, subject to the constraint that the gain should exceed unity for all array responses in this ellipsoid. The robust weight selection process can be cast as a second-order cone program that can be solved efficiently using Lagrange multiplier techniques. If the ellipsoid reduces to a single point, the method coincides with Capon's method. We describe in detail several methods that can be used to derive an appropriate uncertainty ellipsoid for the array response. We form separate uncertainty ellipsoids for each component in the signal path (e.g., antenna, electronics) and then determine an aggregate uncertainty ellipsoid from these. We give new results for modeling the element-wise products of ellipsoids. We demonstrate the robust beamforming and the ellipsoidal modeling methods with several numerical examples. View full abstract»

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  • Blind spatial signature estimation via time-varying user power loading and parallel factor analysis

    Publication Year: 2005 , Page(s): 1697 - 1710
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB) |  | HTML iconHTML  

    In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Crame´r-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensor array manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation. View full abstract»

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  • Source localization by spatially distributed electronic noses for advection and diffusion

    Publication Year: 2005 , Page(s): 1711 - 1719
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (632 KB) |  | HTML iconHTML  

    Based on continuous concentration measurements from spatially distributed electronic noses, the location of a point source is to be determined. It is assumed that the emitted substance is transported by advection caused by a known homogeneous wind field and by isotropic diffusion. A new two-step approach for solving the source localization problem is presented. In the first step, for each sensor i, the set of points Pi is determined, on which the source can lie, taking only the specific concentration measurement Ci at sensor i into account. In the second step, an estimate for the source position is evaluated by intersecting the sets Pi. The new approach overcomes the problem of poor convergence of iterative algorithms, which try to minimize the least squares output error. Finally, experimental results showing the capability of the new approach are presented. View full abstract»

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  • Damped and delayed sinusoidal model for transient signals

    Publication Year: 2005 , Page(s): 1720 - 1730
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (520 KB) |  | HTML iconHTML  

    In this work, we present the Damped and Delayed Sinusoidal (DDS) model: a generalization of the sinusoidal model. This model takes into account an angular frequency, a damping factor, a phase, an amplitude, and a time-delay parameter for each component. Two algorithms are introduced for the DDS parameter estimation using a subband processing approach. Finally, we derive the Crame´r-Rao Bound (CRB) expression for the DDS model and a simulation-based performance analysis in the context of a noisy fast time-varying synthetic signal and in the audio transient signal modeling context. View full abstract»

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  • Robust H filtering for Uncertain2-D continuous systems

    Publication Year: 2005 , Page(s): 1731 - 1738
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (792 KB) |  | HTML iconHTML  

    This paper considers the problem of robust H filtering for uncertain two-dimensional (2-D) continuous systems described by the Roesser state-space model. The parameter uncertainties are assumed to be norm-bounded in both the state and measurement equations. The purpose is the design of a 2-D continuous filter such that for all admissible uncertainties, the error system is asymptotically stable, and the H norm of the transfer function, from the noise signal to the estimation error, is below a prespecified level. A sufficient condition for the existence of such filters is obtained in terms of a set of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desired H filter is given. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method. View full abstract»

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  • Theory and design of multirate sensor arrays

    Publication Year: 2005 , Page(s): 1739 - 1753
    Cited by:  Papers (9)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1424 KB)  

    This paper studies the basic design challenges associated with multirate sensor arrays. A multirate sensor array is a sensor array in which each sensor node communicates a low-resolution measurement to a central processing unit. The objective is to design the individual sensor nodes and the central processing unit such that, at the end, a unified high-resolution measurement is reconstructed. A multirate sensor array can be modeled as an analysis filterbank in discrete-time. Using this model, the design problem is reduced to solving the following two problems: a) how to design the sensor nodes such that the time-delay of arrival (TDOA) between the sensors can be estimated and b) how to design a synthesis filterbank to fuse the low-rate data sent by the sensor nodes given the TDOA? In this paper, we consider a basic two-channel sensor array. We show that it is possible to estimate the TDOA between the sensors if the analysis filters incorporated in the array satisfy specific phase-response requirements. We then provide practical sample designs that satisfy these requirements. We prove, however, that a fixed synthesis filterbank cannot reconstruct the desired high-resolution measurement for all TDOA values. As a result, we suggest a fusion system that uses different sets of synthesis filters for even and odd TDOAs. Finally, we use the H optimality theory to design optimal synthesis filters. View full abstract»

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  • Armlets and balanced multiwavelets: flipping filter construction

    Publication Year: 2005 , Page(s): 1754 - 1767
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (800 KB) |  | HTML iconHTML  

    In the scalar-valued setting, it is well-known that the two-scale sequences {qk} of Daubechies orthogonal wavelets can be given explicitly by the two-scale sequences {pk} of their corresponding orthogonal scaling functions, such as qk=(-1)kp1-k. However, due to the noncommutativity of matrix multiplication, there is little such development in the multiwavelet literature to express the two-scale matrix sequence {Qk} of an orthogonal multiwavelet in terms of the two-scale matrix sequence {Pk} of its corresponding scaling function vector. This paper, in part, is devoted to this study for the setting of orthogonal multiwavelets of dimension r=2. In particular, the two lowpass filters are flipping filters, whereas the two highpass filters are linear phase. These results will be applied to constructing both a family of the most recently introduced notion of armlet of order n and a family of n-balanced orthogonal multiwavelets. View full abstract»

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  • Optimization of two-dimensional IIR filters with nonseparable and separable denominator

    Publication Year: 2005 , Page(s): 1768 - 1777
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB) |  | HTML iconHTML  

    We present algorithms for the optimization of two-dimensional (2-D) infinite impulse response (IIR) filters with separable or nonseparable denominator, for least squares or Chebyshev criteria. The algorithms are iterative, and each iteration consists of solving a semidefinite programming problem. For least squares designs, we adapt the Gauss-Newton idea, which outcomes to a convex approximation of the optimization criterion. For Chebyshev designs, we adapt the iterative reweighted least squares (IRLS) algorithm; in each iteration, a least squares Gauss-Newton step is performed, while the weights are changed as in the basic IRLS algorithm. The stability of the 2-D IIR filters is ensured by keeping the denominator inside convex stability domains, which are defined by linear matrix inequalities. For the 2-D (nonseparable) case, this is a new contribution, based on the parameterization of 2-D polynomials that are positive on the unit bicircle. In the experimental section, 2-D IIR filters with separable and nonseparable denominators are designed and compared. We show that each type may be better than the other, depending on the design specification. We also give an example of filter that is clearly better than a recent very good design. View full abstract»

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  • A new approach for estimation of statistically matched wavelet

    Publication Year: 2005 , Page(s): 1778 - 1793
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB)  

    This paper presents a new approach for the estimation of wavelets that is matched to a given signal in the statistical sense. Based on this approach, a number of new methods to estimate statistically matched wavelets are proposed. The paper first proposes a new method for the estimation of statistically matched two-band compactly supported biorthogonal wavelet system. Second, a new method is proposed to estimate statistically matched semi-orthogonal two-band wavelet system that results in compactly supported or infinitely supported wavelet. Next, the proposed method of estimating two-band wavelet system is generalized to M-band wavelet system. Here, the key idea lies in the estimation of analysis wavelet filters from a given signal. This is similar to a sharpening filter used in image enhancement. The output of analysis highpass filter branch is viewed to be equivalent to an error in estimating the middle sample from the neighborhood. To minimize this error, a minimum mean square error (MMSE) criterion is employed. Since wavelet expansion acts like Karhunen-Loe`ve-type expansion for generalized 1/fβ processes, it is assumed that the given signal is a sample function of an mth-order fractional Brownian motion. Therefore, the autocorrelation structure of a generalized 1/fβ process is used in the estimation of analysis filters using the MMSE criterion. We then present methods to design a finite impulse response/infinite impulse response (FIR/IIR) biorthogonal perfect reconstruction filterbank, leading to the estimation of a compactly supported/infinitely supported statistically matched wavelet. The proposed methods are very simple. Simulation results to validate the proposed theory are presented for different synthetic self-similar signals as well as music and speech clips. Estimated wavelets for different signals are compared with standard biorthogonal 9/7 and 5/3 wavelets for the application of compression and are shown to have better results. View full abstract»

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  • Phaselets of framelets

    Publication Year: 2005 , Page(s): 1794 - 1806
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    Phaselets are a set of dyadic wavelets that are related in a particular way such that the associated redundant wavelet transform is nearly shift-invariant. Framelets are a set of functions that generalize the notion of a single dyadic wavelet in the sense that dyadic dilates and translates of these functions form a frame in L2(IR). This paper generalizes the notion of phaselets to framelets. Sets of framelets that only differ in their Fourier transform phase are constructed such that the resulting redundant wavelet transform is approximately shift invariant. Explicit constructions of phaselets are given for frames with two and three framelet generators. The results in this paper generalize the construction of Hilbert transform pairs of framelets. View full abstract»

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  • Direct projection decoding algorithm for sigma-delta modulated signals

    Publication Year: 2005 , Page(s): 1807 - 1814
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (368 KB) |  | HTML iconHTML  

    Usually, sigma-delta modulators (ΣΔM) are modeled by replacing the quantizer with an additive white noise source. Based on this linear model, linear decoder structures can be designed. However, the ΣΔM is a nonlinear system, and corresponding nonlinear decoder structures may be able to achieve a better signal-to-quantization-noise ratio (SQNR) performance. In this paper, a new nonlinear decoding algorithm is presented. It is based on the projection onto complex sets (POCS) algorithm developed by Hein and Zakhor. Our algorithm reduces the decoding problem to the solution of a single quadratic programming problem based on the state equations of the modulator and the condition that the modulator input signal is bandlimited. The bandlimitation constraint is directly applied to the state equations. Thus, the resulting quadratic programming problem needs to be solved only once. View full abstract»

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  • Testing for stochastic independence: application to blind source separation

    Publication Year: 2005 , Page(s): 1815 - 1826
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    In this paper, we address the issue of testing for stochastic independence and its application as a guide to selecting the standard independent component analysis (ICA) algorithms when solving blind source separation (BSS) problems. Our investigation focuses on the problem of establishing tests for the quality of separation among recovered sources obtained by ICA algorithms in an unsupervised environment. We review existing tests and propose two contingency table-based algorithms. The first procedure is based on the measure of goodness-of-fit of the observed signals to the model of independence provided by the power-divergence (PD) family of test statistics. We provide conditions that guarantee the validity of the independence test when the individual sources are nonstationary. When the sources exhibit significant time dependence, we show how to adopt Hotelling's T2 test statistic for zero mean to create an accurate test of independence. Experimental results obtained from a variety of synthetic and real-life benchmark data sets confirm the success of the PD-based test when the individual source samples preserve the so-called constant cell probability assumption as well as the validity of the T2-based test for sources with significant time dependence. View full abstract»

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  • Nonlinear adaptive prediction of complex-valued signals by complex-valued PRNN

    Publication Year: 2005 , Page(s): 1827 - 1836
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    A complex-valued pipelined recurrent neural network (CPRNN) for nonlinear adaptive prediction of complex nonlinear and nonstationary signals is introduced. This architecture represents an extension of the recently proposed real-valued PRNN of Haykin and Li in 1995. To train the CPRNN, a complex-valued real time recurrent learning (CRTRL) algorithm is first derived for a single recurrent neural network (RNN). This algorithm is shown to be generic and applicable to general signals that have complex domain representations. The CRTRL is then extended to suit the modularity of the CPRNN architecture. Further, to cater to the possibly large dynamics of the input signals, a gradient adaptive amplitude of the nonlinearity within the neurons is introduced to give the adaptive amplitude CRTRL (AACRTRL). A comprehensive analysis of the architecture and associated learning algorithms is undertaken, including the role of the number of nested modules, number of neurons within the modules, and input memory of the CPRNN. Simulations on real-world and synthetic complex data support the proposed architecture and algorithms. View full abstract»

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  • Equalization with oversampling in multiuser CDMA systems

    Publication Year: 2005 , Page(s): 1837 - 1851
    Cited by:  Papers (3)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (736 KB) |  | HTML iconHTML  

    Some of the major challenges in the design of new-generation wireless mobile systems are the suppression of multiuser interference (MUI) and inter-symbol interference (ISI) within a single user created by the multipath propagation. Both of these problems were addressed successfully in a recent design of A Mutually Orthogonal Usercode-Receiver (AMOUR) for asynchronous or quasisynchronous code division multiple access (CDMA) systems. AMOUR converts a multiuser CDMA system into parallel single-user systems regardless of the multipath and guarantees ISI mitigation, irrespective of the channel locations. However, the noise amplification at the receiver can be significant in some multipath channels. In this paper, we propose to oversample the received signal as a way of improving the performance of AMOUR systems. We design Fractionally Spaced AMOUR (FSAMOUR) receivers with integral and rational amounts of oversampling and compare their performance with the conventional method. An important point that is often overlooked in the design of zero-forcing channel equalizers is that sometimes, they are not unique. This becomes especially significant in multiuser applications where, as we will show, the nonuniqueness is practically guaranteed. We exploit this flexibility in the design of AMOUR and FSAMOUR receivers and achieve noticeable improvements in performance. View full abstract»

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  • Noise-predictive decision-feedback detection for multiple-input multiple-output channels

    Publication Year: 2005 , Page(s): 1852 - 1859
    Cited by:  Papers (12)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB) |  | HTML iconHTML  

    The decision-feedback (DF) detector is a nonlinear detection strategy for multiple-input multiple-output (MIMO) channels that can significantly outperform a linear detector, especially when the order in which the inputs are detected is optimized according to the so-called Bell Labs Layered Space-Time (BLAST) ordering. The DF detector may be implemented as the cascade of a linear detector, which mitigates interference at the expense of correlating the noise, followed by a noise predictor, which exploits the correlation in the noise to reduce its variance. With this architecture, existing linear detectors can be easily upgraded to DF detectors. We propose a low-complexity algorithm for determining the BLAST ordering that is facilitated by the noise-predictive architecture. The resulting ordered noise-predictive DF detector requires fewer computations than previously reported ordered-DF algorithms. We also propose and derive the ordered noise-predictive minimum-mean-squared-error DF detector and show how to determine its BLAST ordering with low complexity. View full abstract»

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  • Blind equalization for correlated input symbols: A Bussgang approach

    Publication Year: 2005 , Page(s): 1860 - 1869
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1336 KB)  

    This paper addresses the problem of blind equalization in the case of correlated input symbols, and it shows how the knowledge of the symbol sequence probability distribution can be directly incorporated in a Bussgang blind equalization scheme. Numerical results pertaining to both linear and nonlinear modulation schemes show that a significant improvement in equalization performance is obtained by exploiting the symbol sequence probability distribution using the approach herein described. View full abstract»

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  • Low noise reversible MDCT (RMDCT) and its application in progressive-to-lossless embedded audio coding

    Publication Year: 2005 , Page(s): 1870 - 1880
    Cited by:  Papers (6)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB) |  | HTML iconHTML  

    A reversible transform converts an integer input to an integer output, while retaining the ability to reconstruct the exact input from the output sequence. It is one of the key components for lossless and progressive-to-lossless audio codecs. In this work, we investigate the desired characteristics of a high-performance reversible transform. Specifically, we show that the smaller the quantization noise of the reversible modified discrete cosine transform (RMDCT), the better the compression performance of the lossless and progressive-to-lossless codec that utilizes the transform. Armed with this knowledge, we develop a number of RMDCT solutions. The first RMDCT solution is implemented by turning every rotation module of a float MDCT (FMDCT) into a reversible rotation, which uses multiple factorizations to further reduce the quantization noise. The second and third solutions use the matrix lifting to implement a reversible fast Fourier transform (FFT) and a reversible fractional-shifted FFT, respectively, which are further combined with the reversible rotations to form the RMDCT. With the matrix lifting, we can design the RMDCT that has less quantization noise and can still be computed efficiently. A progressive-to-lossless embedded audio codec (PLEAC) employing the RMDCT is implemented with superior results for both lossless and lossy audio compression. 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