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Signal Processing, IEEE Transactions on

Issue 11 • Date Nov. 2012

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Displaying Results 1 - 25 of 55
  • Table of Contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Signal Processing publication information

    Page(s): C2
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    Freely Available from IEEE
  • Complex Elliptically Symmetric Distributions: Survey, New Results and Applications

    Page(s): 5597 - 5625
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7476 KB) |  | HTML iconHTML  

    Complex elliptically symmetric (CES) distributions have been widely used in various engineering applications for which non-Gaussian models are needed. In this overview, circular CES distributions are surveyed, some new results are derived and their applications e.g., in radar and array signal processing are discussed and illustrated with theoretical examples, simulations and analysis of real radar data. The maximum likelihood (ML) estimator of the scatter matrix parameter is derived and general conditions for its existence and uniqueness, and for convergence of the iterative fixed point algorithm are established. Specific ML-estimators for several CES distributions that are widely used in the signal processing literature are discussed in depth, including the complex t -distribution, K-distribution, the generalized Gaussian distribution and the closely related angular central Gaussian distribution. A generalization of ML-estimators, the M-estimators of the scatter matrix, are also discussed and asymptotic analysis is provided. Applications of CES distributions and the adaptive signal processors based on ML- and M-estimators of the scatter matrix are illustrated in radar detection problems and in array signal processing applications for Direction-of-Arrival (DOA) estimation and beamforming. Furthermore, experimental validation of the usefulness of CES distributions for modelling real radar data is given. View full abstract»

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  • Separating Function Estimation Tests: A New Perspective on Binary Composite Hypothesis Testing

    Page(s): 5626 - 5639
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5786 KB) |  | HTML iconHTML  

    In this paper, we study some relationships between the detection and estimation theories for a binary composite hypothesis test H0 against H1 and a related estimation problem. We start with a one-dimensional (1D) space for the unknown parameter space and one-sided hypothesis problems and then extend out results into more general cases. For one-sided tests, we show that the uniformly most powerful (UMP) test is achieved by comparing the minimum variance and unbiased estimator (MVUE) of the unknown parameter with a threshold. Thus for the case where the UMP test does not exist, the MVUE of the unknown parameter does not exist either. Therefore for such cases, a good estimator of the unknown parameter is deemed as a good decision statistic for the test. For a more general class of composite testing with multiple unknown parameters, we prove that the MVUE of a separating function (SF) can serve as the optimal decision statistic for the UMP unbiased test where the SF is continuous, differentiable, positive for all parameters under H1 and is negative for the parameters under H0. We then prove that the UMP unbiased statistic is equal to the MVUE of an SF. In many problems with multiple unknown parameters, the UMP test does not exist. For such cases, we show that if one detector between two detectors has a better receiver operating characteristic (ROC) curve, then using its decision statistic we can estimate the SF more ε-accurately, in probability. For example, the SF is the signal-to-noise ratio (SNR) in some problems. These results motivate us to introduce new suboptimal SF-estimator tests (SFETs) which are easy to derive for many problems. Finally, we provide some practical examples to study the relationship between the decision statistic of a test and the estimator of its corresponding SF. View full abstract»

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  • Shrinkage-to-Tapering Estimation of Large Covariance Matrices

    Page(s): 5640 - 5656
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4940 KB) |  | HTML iconHTML  

    In this paper, we introduce a shrinkage-to-tapering approach for estimating large covariance matrices when the number of samples is substantially fewer than the number of variables (i.e., n,p→∞ and p/n→∞). The proposed estimator improves upon both shrinkage and tapering estimators by shrinking the sample covariance matrix to its tapered version. We first show that, under both normalized Frobenius and spectral risks, the minimum mean-squared error (MMSE) shrinkage-to-identity estimator is inconsistent and outperformed by a minimax tapering estimator for a class of high-dimensional and diagonally dominant covariance matrices. Motivated by this observation, we propose a shrinkage-to-tapering oracle (STO) estimator for efficient estimation of general, large covariance matrices. A closed-form formula of the optimal coefficient ρ of the proposed STO estimator is derived under the minimum Frobenius risk. Since the true covariance matrix is to be estimated, we further propose a STO approximating (STOA) algorithm with a data-driven bandwidth selection procedure to iteratively estimate the coefficient ρ and the covariance matrix. We study the finite sample performances of different estimators and our simulation results clearly show the improved performances of the proposed STO estimators. Finally, the proposed STOA method is applied to a real breast cancer gene expression data set. View full abstract»

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  • A phd Filter for Tracking Multiple Extended Targets Using Random Matrices

    Page(s): 5657 - 5671
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3209 KB) |  | HTML iconHTML  

    This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch is adapted into the extended target phd framework, resulting in the Gaussian inverse Wishart phd (giw-phd) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans. View full abstract»

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  • Complex-Valued Linear and Widely Linear Filtering Using MSE and Gaussian Entropy

    Page(s): 5672 - 5684
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3675 KB) |  | HTML iconHTML  

    In this paper, we study the performance of mean square error (MSE) and Gaussian entropy criteria for linear and widely linear complex filtering. The MSE criterion has been extensively studied, and with a widely linear filter form, it can take into account the full second-order statistics of the input signal. However, it cannot exploit the full second-order statistics of the error, and doubles the dimension of the parameter vector to be estimated. In this paper, we introduce the use of Gaussian entropy criterion such that full second-order statistics of the error can be taken into account, and compare the performance of the Gaussian entropy and MSE criteria for a linear and widely linear filter implementation in batch and adaptive implementations. Detailed performance analysis with numerical examples is presented to investigate the relationship and performance differences of the two criteria in diverse scenarios. View full abstract»

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  • On Delay Tomography: Fast Algorithms and Spatially Dependent Models

    Page(s): 5685 - 5697
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3416 KB) |  | HTML iconHTML  

    As an active branch of network tomography, delay tomography has received considerable attentions in recent years. However, most methods in the literature assume that the delays of different links are independent of each other, and pursuit sub-optimal estimate instead of the maximum likelihood estimate (MLE) due to computational challenges. In this paper, we propose a novel method to implement the EM algorithm widely used in delay tomography analysis for multicast networks. The proposed method makes use of a “delay pattern database” to avoid all redundant computations in the E-step, and is much faster than the traditional implementation. With the help of this new implementation, finding MLE for large networks, which was considered impractical previously, becomes an easy task. Taking advantage of this computational breakthrough, we further consider models for potential spatial dependence of links, and propose a novel adaptive spatially dependent model (ASDM) for delay tomography. In ASDM, Markov dependence among nearby links is allowed, and spatially dependent links (SDLs) can be automatically recognized via model selection. The superiority of the new methods is confirmed by simulation studies. View full abstract»

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  • CONFAC Decomposition Approach to Blind Identification of Underdetermined Mixtures Based on Generating Function Derivatives

    Page(s): 5698 - 5713
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4311 KB) |  | HTML iconHTML  

    This work proposes a new tensor-based approach to solve the problem of blind identification of underdetermined mixtures of complex-valued sources exploiting the cumulant generating function (CGF) of the observations. We show that a collection of second-order derivatives of the CGF of the observations can be stored in a third-order tensor following a constrained factor (CONFAC) decomposition with known constrained structure. In order to increase the diversity, we combine three derivative types into an extended third-order CONFAC decomposition. A detailed uniqueness study of this decomposition is provided, from which easy-to-check sufficient conditions ensuring the essential uniqueness of the mixing matrix are obtained. From an algorithmic viewpoint, we develop a CONFAC-based enhanced line search (CONFAC-ELS) method to be used with an alternating least squares estimation procedure for accelerated convergence, and also analyze the numerical complexities of two CONFAC-based algorithms (namely, CONFAC-ALS and CONFAC-ELS) in comparison with the Levenberg-Marquardt (LM)-based algorithm recently derived to solve the same problem. Simulation results compare the proposed approach with some higher-order methods. Our results also corroborate the advantages of the CONFAC-based approach over the competing LM-based approach in terms of performance and computational complexity. View full abstract»

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  • On l_q Optimization and Matrix Completion

    Page(s): 5714 - 5724
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3862 KB) |  | HTML iconHTML  

    Rank minimization problems, which consist of finding a matrix of minimum rank subject to linear constraints, have been proposed in many areas of engineering and science. A specific problem is the matrix completion problem in which a low rank data matrix can be recovered from incomplete samples of its entries by solving a rank penalized least squares problem. The rank penalty is in fact the l0 “norm” of the matrix singular values. A recent convex relaxation of this penalty is the commonly used l1 norm of the matrix singular values. In this paper, we bridge the gap between these two penalties and propose the lq, 0 <; q <; 1 penalized least squares problem for matrix completion. An iterative algorithm is developed by solving a non-standard optimization problem and a non-trivial convergence result is proved. We illustrate with simulations comparing the reconstruction quality of the three matrix singular value penalty functions: l0, l1, and lq, 0 <; q <; 1. View full abstract»

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  • Reconstruction of Sparse Signals From \ell _1 Dimensionality-Reduced Cauchy Random Projections

    Page(s): 5725 - 5737
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2783 KB) |  | HTML iconHTML  

    Dimension reduction methods via linear random projections are used in numerous applications including data mining, information retrieval and compressive sensing (CS). While CS has traditionally relied on normal random projections, corresponding to ℓ2 distance preservation, a large body of work has emerged for applications where ℓ1 approximate distances may be preferred. Dimensionality reduction in ℓ1 often use Cauchy random projections that multiply the original data matrix B ∈ Rn×D with a Cauchy random matrix R ∈k×n (k≪n), resulting in a projected matrix C ∈k×D. In this paper, an analogous of the Restricted Isometry Property for dimensionality reduction in is ℓ1 proposed using explicit tail bounds for the geometric mean of the random projections. A set of signal reconstruction algorithms from the Cauchy random projections are then developed given that the large suite of reconstruction algorithms developed in compressive sensing perform poorly due to the lack of finite second-order statistics in the projections. These algorithms are based on regularized coordinate-descent Myriad estimates using both ℓ0 and Lorentzian norms as sparsity inducing terms. View full abstract»

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  • Clock Jitter Compensation in High-Rate ADC Circuits

    Page(s): 5738 - 5753
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3864 KB) |  | HTML iconHTML  

    Clock timing jitter refers to random perturbations in the sampling time in analog-to-digital converters (ADCs). The perturbations are caused by circuit imperfections in the sampling clock. This paper analyzes the effect of sampling clock jitter on the acquired samples in the midst of random noise. We propose low-complexity digital signal processing methods for estimating the jitter in real-time for direct downconversion receivers at high sampling rates. We also propose adaptive compensation methods for the jitter and analyze the performance of the proposed techniques in some detail as well as through simulations. View full abstract»

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  • Arbitrary Partial FEXT Cancellation in Adaptive Precoding for Multichannel Downstream VDSL

    Page(s): 5754 - 5763
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2637 KB) |  | HTML iconHTML  

    In this paper, we present and analyze a simplified, adaptive precoder for arbitrary partial far-end cross talk (FEXT) cancellation in the downstream of multichannel VDSL. The precoder is based on error signal feedback and is computationally efficient. Furthermore, the partial precoding makes it possible to operate the precoder at any desired complexity, and the system's performance increases with any increase in system complexity. Unlike previous works, in which each user experienced either complete FEXT cancellation or no cancellation at all, the presented precoder can mitigate the FEXT from any desired subset of interfering users for each user. We derive sufficient conditions for convergence of the adaptive precoder, for any partial cancellation scheme, and provide closed form steady state error analysis. The precoder's performance and convergence properties are also demonstrated through simulations. View full abstract»

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  • IIR-Based {\rm DT}{cal C}{\rm WTs} With Improved Analyticity and Frequency Selectivity

    Page(s): 5764 - 5774
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2817 KB) |  | HTML iconHTML  

    In this paper, a new class of DTCWTs with improved analyticity and frequency selectivity is proposed by using general IIR filters with numerator and denominator of different degree. In the common-factor technique proposed by Selesnick, the maximally flat allpass filter was used to satisfy the half-sample delay condition. Thus, to improve the analyticity of complex wavelets, we present a method for designing allpass filters with the specified degree of flatness and equiripple phase response in the approximation band. Furthermore, to improve the frequency selectivity of scaling lowpass filters, we locate the specified number of zeros at z = -1 and minimize the stopband error. The design methods proposed in this paper use the well-known Remez exchange algorithm to approximate the equiripple response. Therefore, a set of filter coefficients can be easily obtained by solving the eigenvalue problem. Finally, we investigate the performance on the proposed DTCWTs through several design examples. It is shown that the conventional DTCWTs proposed by Selesnick are only the special cases of DTCWTs proposed in this paper. View full abstract»

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  • Response of Dynamical Systems to Nonstationary Inputs

    Page(s): 5775 - 5786
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2359 KB) |  | HTML iconHTML  

    We obtain the time-frequency response of the output of a dynamical system when the input belongs to a class of common nonstationary signals, namely, an impulse, a linear chirp, a causal sinusoid, and a short duration sinusoid. The obtained results clarify how the system processes the time-varying frequencies of the input signal to generate the time-frequency spectrum of the output. All analytic results are exact. The solution is obtained by developing a method which can be used to evaluate the output of a dynamical system for complex combinations of nonstationary inputs. We show numerical examples which prove that the response of a system to a nonstationary input is made by a series of events occurring in the joint time-frequency domain. View full abstract»

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  • A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising

    Page(s): 5787 - 5798
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3267 KB) |  | HTML iconHTML  

    In this paper, we address the problem of the retrieval of the components from a multicomponent signal using ideas from the synchrosqueezing framework. The emphasis is on the wavelet choice and we propose a novel algorithm based first on the detection of components followed by their reconstruction. Simulations illustrate how the proposed procedure compares with the empirical mode decomposition and other related methods in terms of mode-mixing. We conclude the paper by studying the sensitivity of the proposed technique to sampling and an application to signal denoising. View full abstract»

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  • Improving Noise Robustness in Subspace-Based Joint Sparse Recovery

    Page(s): 5799 - 5809
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2466 KB) |  | HTML iconHTML  

    In a multiple measurement vector problem (MMV), where multiple signals share a common sparse support and are sampled by a common sensing matrix, we can expect joint sparsity to enable a further reduction in the number of required measurements. While a diversity gain from joint sparsity had been demonstrated earlier in the case of a convex relaxation method using an l1/ l2 mixed norm penalty, only recently was it shown that similar diversity gain can be achieved by greedy algorithms if we combine greedy steps with a MUSIC-like subspace criterion. However, the main limitation of these hybrid algorithms is that they often require a large number of snapshots or a high signal-to-noise ratio (SNR) for an accurate subspace as well as partial support estimation. One of the main contributions of this work is to show that the noise robustness of these algorithms can be significantly improved by allowing sequential subspace estimation and support filtering, even when the number of snapshots is insufficient. Numerical simulations show that a novel sequential compressive MUSIC (sequential CS-MUSIC) that combines the sequential subspace estimation and support filtering steps significantly outperforms the existing greedy algorithms and is quite comparable with computationally expensive state-of-art algorithms. View full abstract»

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  • Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition

    Page(s): 5810 - 5819
    Multimedia
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    We develop new nonconvex approaches for matrix optimization problems involving sparsity. The heart of the methods is a new, nonconvex penalty function that is designed for efficient minimization by means of a generalized shrinkage operation. We apply this approach to the decomposition of video into low rank and sparse components, which is able to separate moving objects from the stationary background better than in the convex case. In the case of noisy data, we add a nonconvex regularization, and apply a splitting approach to decompose the optimization problem into simple, parallelizable components. The nonconvex regularization ameliorates contrast loss, thereby allowing stronger denoising without losing more signal to the residual. View full abstract»

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  • Clustering With Multi-Layer Graphs: A Spectral Perspective

    Page(s): 5820 - 5831
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2107 KB) |  | HTML iconHTML  

    Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (objects) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer graph for an improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on a joint matrix factorization and a graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely the eigenvectors of the graph Laplacian matrices. In each case, the resulting combination, which we call a “joint spectrum” of multiple layers, is used for clustering the vertices. We evaluate our approaches by experiments with several real world social network datasets. Results demonstrate the superior or competitive performance of the proposed methods compared to state-of-the-art techniques and common baseline methods, such as co-regularization and summation of information from individual graphs. View full abstract»

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  • Blind Compensation of Nonlinear Distortions: Application to Source Separation of Post-Nonlinear Mixtures

    Page(s): 5832 - 5844
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2586 KB) |  | HTML iconHTML  

    In this paper, we address the problem of blind compensation of nonlinear distortions. Our approach relies on the assumption that the input signal is bandlimited. We then make use of the classical result that the output of a nonlinearity has a wider spectrum than the one of the input signal. However, differently from previous works, our approach does not assume knowledge of the input signal bandwidth. The proposal is considered in the development of a two-stage method for blind source separation (BSS) in post-nonlinear (PNL) models. Indeed, once the functions present in the nonlinear stage of a PNL model are compensated, one can apply the well-established linear BSS algorithms to complete the task of separating the sources. Numerical experiments performed in different scenarios attest the viability of the proposal. Moreover, the proposed method is tested in a real situation where the data are acquired by smart chemical sensor arrays. View full abstract»

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  • Linearly Constrained Robust Capon Beamforming

    Page(s): 5845 - 5856
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3129 KB) |  | HTML iconHTML  

    In this paper, a novel linearly constrained robust Capon beamformer (LCRCB) framework is proposed. In the LCRCB, linear constraints can be used, e.g., for beampattern control and ellipsoidal array steering vector sets can be exploited, using robust Capon beamforming techniques, e.g., to allow for arbitrary array steering vector errors, such as those arising from calibration errors. The LCRCB is applicable to arbitrary array geometries and can be computed efficiently. For the limiting case that the ellipsoid is a point, we show that the LCRCB coincides with a linearly constrained minimum variance beamformer. To show the utility of the LCRCB, mainbeam and null-pattern control examples are included. View full abstract»

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  • Efficient Calculation of Sensor Utility and Sensor Removal in Wireless Sensor Networks for Adaptive Signal Estimation and Beamforming

    Page(s): 5857 - 5869
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2454 KB) |  | HTML iconHTML  

    Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called “utility” of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of K different signal estimators (where K is the number of sensors), increasing computational complexity and memory usage by a factor K. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations. View full abstract»

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  • Generalized Sampling Expansion for Functions on the Sphere

    Page(s): 5870 - 5879
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2115 KB) |  | HTML iconHTML  

    Functions on the sphere appear in several applications, including geodesics, imaging and acoustics. Sampling of these functions may result in aliasing if the sampling condition is not met. The generalized sampling expansion introduced by Papoulis enables the reconstruction of a band-limited function sampled at a frequency lower than the Nyquist frequency using the outputs of several linear time-invariant systems. This paper formulates the generalized sampling expansion for functions on the sphere using spherical harmonics decomposition, facilitating sub-Nyquit sampling without aliasing error. An analysis of linear systems on the sphere and the aliasing phenomenon in the spherical harmonics domain is presented. Examples demonstrating the performance of the method and its limitations are studied. View full abstract»

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  • Multiple Description Analog Joint Source-Channel Coding to Exploit the Diversity in Parallel Channels

    Page(s): 5880 - 5892
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2206 KB) |  | HTML iconHTML  

    The complexity and delay introduced by efficient digital coding strategies may be a barrier in some real-time communications. In this sense, these last years, joint source-channel coding schemes based on analog mappings have gained prominence precisely for their simplicity and their implicit low delay. In this work, analog mappings originally designed for point-to-point communications are adapted to the case of parallel channels by following the multiple description strategy traditionally used in source coding. In principle, the coding scheme is designed to transmit over parallel AWGN on-off channels, which are characterized by the possibility of having failures. We also show that our scheme performs satisfactorily over slow Rayleigh fading parallel channels. View full abstract»

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  • Eigenvalue Estimation of Parameterized Covariance Matrices of Large Dimensional Data

    Page(s): 5893 - 5905
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3219 KB) |  | HTML iconHTML  

    This article deals with the problem of estimating the covariance matrix of a series of independent multivariate observations, in the case where the dimension of each observation is of the same order as the number of observations. Although such a regime is of interest for many current statistical signal processing and wireless communication issues, traditional methods fail to produce consistent estimators and only recently results relying on large random matrix theory have been unveiled. In this paper, we develop the parametric framework proposed by Mestre, and consider a model where the covariance matrix to be estimated has a (known) finite number of eigenvalues, each of it with an unknown multiplicity. The main contributions of this work are essentially threefold with respect to existing results, and in particular to Mestre's work: To relax the (restrictive) separability assumption, to provide joint consistent estimates for the eigenvalues and their multiplicities, and to study the variance error by means of a Central Limit Theorem. 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