# Showing papers in "IEEE Transactions on Signal Processing in 2002"

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TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

11,409 citations

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TL;DR: The technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map and it is shown that the accuracy is comparable with satellite navigation but with higher integrity.

Abstract: A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

1,787 citations

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TL;DR: This work proves sampling theorems for classes of signals and kernels that generalize the classic "bandlimited and sinc kernel" case and shows how to sample and reconstruct periodic and finite-length streams of Diracs, nonuniform splines, and piecewise polynomials using sinc and Gaussian kernels.

Abstract: The authors consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials. Even though these signals are not bandlimited, we show that they can be sampled uniformly at (or above) the rate of innovation using an appropriate kernel and then be perfectly reconstructed. Thus, we prove sampling theorems for classes of signals and kernels that generalize the classic "bandlimited and sinc kernel" case. In particular, we show how to sample and reconstruct periodic and finite-length streams of Diracs, nonuniform splines, and piecewise polynomials using sinc and Gaussian kernels. For infinite-length signals with finite local rate of innovation, we show local sampling and reconstruction based on spline kernels. The key in all constructions is to identify the innovative part of a signal (e.g., time instants and weights of Diracs) using an annihilating or locator filter: a device well known in spectral analysis and error-correction coding. This leads to standard computational procedures for solving the sampling problem, which we show through experimental results. Applications of these new sampling results can be found in signal processing, communications systems, and biological systems.

1,206 citations

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TL;DR: This work proposes an intermediate virtual channel representation that captures the essence of physical modeling and provides a simple geometric interpretation of the scattering environment and shows that in an uncorrelated scattering environment, the elements of the channel matrix form a segment of a stationary process and that the virtual channel coefficients are approximately uncor related samples of the underlying spectral representation.

Abstract: Accurate and tractable channel modeling is critical to realizing the full potential of antenna arrays in wireless communications. Current approaches represent two extremes: idealized statistical models representing a rich scattering environment and parameterized physical models that describe realistic scattering environments via the angles and gains associated with different propagation paths. However, simple rules that capture the effects of scattering characteristics on channel capacity and diversity are difficult to infer from existing models. We propose an intermediate virtual channel representation that captures the essence of physical modeling and provides a simple geometric interpretation of the scattering environment. The virtual representation corresponds to a fixed coordinate transformation via spatial basis functions defined by fixed virtual angles. We show that in an uncorrelated scattering environment, the elements of the channel matrix form a segment of a stationary process and that the virtual channel coefficients are approximately uncorrelated samples of the underlying spectral representation. For any scattering environment, the virtual channel matrix clearly reveals the two key factors affecting capacity: the number of parallel channels and the level of diversity. The concepts of spatial zooming and aliasing are introduced to provide a transparent interpretation of the effect of antenna spacing on channel statistics and capacity. Numerical results are presented to illustrate various aspects of the virtual framework.

1,106 citations

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TL;DR: This work proposes new non-Gaussian bivariate distributions, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory, but the new shrinkage functions do not assume the independence of wavelet coefficients.

Abstract: Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. We only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory. The new shrinkage functions do not assume the independence of wavelet coefficients. We show three image denoising examples in order to show the performance of these new bivariate shrinkage rules. In the second example, a simple subband-dependent data-driven image denoising system is described and compared with effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and hidden Markov models. In the third example, the same idea is applied to the dual-tree complex wavelet coefficients.

1,048 citations

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TL;DR: A discrete-time analysis of the orthogonal frequency division multiplex/offset QAM (OFDM/OQAM) multicarrier modulation technique, leading to a modulated transmultiplexer, is presented.

Abstract: A discrete-time analysis of the orthogonal frequency division multiplex/offset QAM (OFDM/OQAM) multicarrier modulation technique, leading to a modulated transmultiplexer, is presented. The conditions of discrete orthogonality are established with respect to the polyphase components of the OFDM/OQAM prototype filter, which is assumed to be symmetrical and with arbitrary length. Fast implementation schemes of the OFDM/OQAM modulator and demodulator are provided, which are based on the inverse fast Fourier transform. Non-orthogonal prototypes create intersymbol and interchannel interferences (ISI and ICI) that, in the case of a distortion-free transmission, are expressed by a closed-form expression. A large set of design examples is presented for OFDM/OQAM systems with the number of subcarriers going from four up to 2048, which also allows a comparison between different approaches to get well-localized prototypes.

1,020 citations

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TL;DR: The aim of this paper is to present a survey of convergence results on particle filtering methods to make them accessible to practitioners.

Abstract: Optimal filtering problems are ubiquitous in signal processing and related fields. Except for a restricted class of models, the optimal filter does not admit a closed-form expression. Particle filtering methods are a set of flexible and powerful sequential Monte Carlo methods designed to. solve the optimal filtering problem numerically. The posterior distribution of the state is approximated by a large set of Dirac-delta masses (samples/particles) that evolve randomly in time according to the dynamics of the model and the observations. The particles are interacting; thus, classical limit theorems relying on statistically independent samples do not apply. In this paper, our aim is to present a survey of convergence results on this class of methods to make them accessible to practitioners.

1,013 citations

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TL;DR: This work explores a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion and shows that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction.

Abstract: A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities over the transmitted symbols. A number of reduced-complexity methods for turbo equalization have been introduced in which MAP equalization is replaced with suboptimal, low-complexity approaches. We explore a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSE-optimal solution. All approaches are qualitatively analyzed by observing the mean-square error averaged over a sequence of equalized data. We show that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction.

985 citations

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Cornell University

^{1}, Uppsala University^{2}, Sapienza University of Rome^{3}, University of Minnesota^{4}TL;DR: A new paradigm for the design of transmitter space-time coding is introduced that is referred to as linear precoding, which leads to simple closed-form solutions for transmission over frequency-selective multiple-input multiple-output (MIMO) channels, which are scalable with respect to the number of antennas, size of the coding block, and transmit average/peak power.

Abstract: We introduce a new paradigm for the design of transmitter space-time coding that we refer to as linear precoding. It leads to simple closed-form solutions for transmission over frequency-selective multiple-input multiple-output (MIMO) channels, which are scalable with respect to the number of antennas, size of the coding block, and transmit average/peak power. The scheme operates as a block transmission system in which vectors of symbols are encoded and modulated through a linear mapping operating jointly in the space and time dimension. The specific designs target minimization of the symbol mean square error and the approximate maximization of the minimum distance between symbol hypotheses, under average and peak power constraints. The solutions are shown to convert the MIMO channel with memory into a set of parallel flat fading subchannels, regardless of the design criterion, while appropriate power/bits loading on the subchannels is the specific signature of the different designs. The proposed designs are compared in terms of various performance measures such as information rate, BER, and symbol mean square error.

891 citations

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TL;DR: This work presents an efficient algorithm to solve a class of two- and 2.5-dimensional Fredholm integrals of the first kind with a tensor product structure and nonnegativity constraint on the estimated parameters of interest in an optimization framework using a zeroth-order regularization functional.

Abstract: We present an efficient algorithm to solve a class of two- and 25-dimensional (2-D and 25-D) Fredholm integrals of the first kind with a tensor product structure and nonnegativity constraint on the estimated parameters of interest in an optimization framework A zeroth-order regularization functional is used to incorporate a priori information about the smoothness of the parameters into the problem formulation We adapt the Butler-Reeds-Dawson (1981) algorithm to solve this optimization problem in three steps In the first step, the data are compressed using singular value decomposition (SVD) of the kernels The tensor-product structure of the kernel is exploited so that the compressed data is typically a thousand fold smaller than the original data This size reduction is crucial for fast optimization In the second step, the constrained optimization problem is transformed to an unconstrained optimization problem in the compressed data space In the third step, a suboptimal value of the smoothing parameter is chosen by the BRD method Steps 2 and 3 are iterated until convergence of the algorithm We demonstrate the performance of the algorithm on simulated data

603 citations

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TL;DR: This paper treats multiple-input multiple-output (MIMO) antenna subset selection employing space-time coding as two cases differentiated based on the type of channel knowledge used in the selection process, and addresses both the selection algorithms and the performance analysis.

Abstract: This paper treats multiple-input multiple-output (MIMO) antenna subset selection employing space-time coding. We consider two cases differentiated based on the type of channel knowledge used in the selection process. We address both the selection algorithms and the performance analysis. We first consider the case when the antenna subsets are selected based on exact channel knowledge (ECK). Our results assume the transmission of orthogonal space-time block codes (with emphasis on the Alamouti (see IEEE J. Select. Areas Commun., vol.16, p.1451-68, Oct. 1998) code). Next, we treat the case of antenna subset selection when statistical channel knowledge (SCK) is employed by the selection algorithm. This analysis is applicable to general space-time coding schemes. When ECK is available, we show that the selection algorithm chooses the antenna set that maximizes the channel Frobenius norm leading to both coding and diversity gain. When SCK is available, the selection algorithm chooses the antenna set that maximizes the determinant of the covariance of the vectorized channel leading mostly to a coding gain. In case of ECK-based selection, we provide analytical expressions for average SNR and outage probability improvement. For the case when SCK-based selection is used, we derive expressions for coding gain. We also present extensive simulation studies, validating our results.

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TL;DR: The proposed maximum-likelihood location estimator for wideband sources in the near field of the sensor array is derived and is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods.

Abstract: In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.

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TL;DR: The authors' optimal transmitter design turns out to be an eigen-beamformer with multiple beams pointing to orthogonal directions along the eigenvectors of the correlation matrix of the estimated channel at the transmitter and with proper power loading across beams.

Abstract: Optimal transmitter designs obeying the water-filling principle are well-documented; they are widely applied when the propagation channel is deterministically known and regularly updated at the transmitter. Because channel state information is impossible to be known perfectly at the transmitter in practical wireless systems, we design, in this paper, an optimal multiantenna transmitter based on the knowledge of mean values of the underlying channels. Our optimal transmitter design turns out to be an eigen-beamformer with multiple beams pointing to orthogonal directions along the eigenvectors of the correlation matrix of the estimated channel at the transmitter and with proper power loading across beams. The optimality pertains to minimizing an upper bound on the symbol error rate, which leads to better performance than maximizing the expected signal-to-noise ratio (SNR) at the receiver. Coupled with orthogonal space-time block codes, two-directional eigen-beamforming emerges as a more attractive choice than conventional one-directional beamforming with uniformly improved performance, without rate reduction, and without essential increase in complexity. With multiple receive antennas and reasonably good feedback quality, the two-directional eigen-beamformer is also capable of achieving the best possible performance in a large range of transmit-power-to-noise ratios, without a rate penalty.

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TL;DR: Novel algorithms that iteratively converge to a local minimum of a real-valued function f (X) subject to the constraint that the columns of the complex-valued matrix X are mutually orthogonal and have unit norm are presented.

Abstract: This paper presents novel algorithms that iteratively converge to a local minimum of a real-valued function f (X) subject to the constraint that the columns of the complex-valued matrix X are mutually orthogonal and have unit norm. The algorithms are derived by reformulating the constrained optimization problem as an unconstrained one on a suitable manifold. This significantly reduces the dimensionality of the optimization problem. Pertinent features of the proposed framework are illustrated by using the framework to derive an algorithm for computing the eigenvector associated with either the largest or the smallest eigenvalue of a Hermitian matrix.

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TL;DR: Particle filters for dynamic state-space models handling unknown static parameters are discussed, based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered.

Abstract: Particle filters for dynamic state-space models handling unknown static parameters are discussed. The approach is based on marginalizing the static parameters out of the posterior distribution such that only the state vector needs to be considered. Such a marginalization can always be applied. However, real-time applications are only possible when the distribution of the unknown parameters given both observations and the hidden state vector depends on some low-dimensional sufficient statistics. Such sufficient statistics are present in many of the commonly used state-space models. Marginalizing the static parameters avoids the problem of impoverishment, which typically occurs when static parameters are included as part of the state vector. The filters are tested on several different models, with promising results.

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TL;DR: This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels with good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms.

Abstract: This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model the effects of inter-symbol interference (ISI), co-channel interference (CCI), and noise. A low-order autoregressive model approximates the MIMO channel variation and facilitates tracking via a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay /spl Delta/ > 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate.

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AT&T

^{1}TL;DR: This paper develops a model for multicarrier transmission over time-varying channels and focuses particularly on multiple-input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM), and proposes a time-domain approach to channel estimation.

Abstract: In this paper, we examine multicarrier transmission over time-varying channels. We first develop a model for such a transmission scheme and focus particularly on multiple-input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM). Using this method, we analyze the impact of time variation within a transmission block (time variation could arise both from Doppler spread of the channel and from synchronization errors). To mitigate the effects of such time variations, we propose a time-domain approach. We design ICI-mitigating block linear filters, and we examine how they are modified in the context of space-time block-coded transmissions. Our approach reduces to the familiar single-tap frequency-domain equalizer when the channel is block time invariant. Channel estimation in rapidly time-varying scenarios becomes critical, and we propose a scheme for estimating channel parameters varying within a transmission block. Along with the channel estimation scheme, we also examine the issue of pilot tone placement and show that in time-varying channels, it may be better to group pilot tones together into clumps that are equispaced onto the FFT grid; this placement technique is in contrast to the common wisdom for time-invariant channels. Finally, we provide numerical results illustrating the performance of these schemes, both for uncoded and space-time block-coded systems.

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TL;DR: This work proposes an iterative alternating-directions algorithm for minimizing the WLS criterion with respect to a general (not necessarily orthogonal) diagonalizing matrix and proves weak convergence in the sense that the norm of parameters update is guaranteed to fall below any arbitrarily small threshold within a finite number of iterations.

Abstract: Approximate joint diagonalization of a set of matrices is an essential tool in many blind source separation (BSS) algorithms. A common measure of the attained diagonalization of the set is the weighted least-squares (WLS) criterion. However, most well-known algorithms are restricted to finding an orthogonal diagonalizing matrix, relying on a whitening phase for the nonorthogonal factor. Often, such an approach implies unbalanced weighting, which can result in degraded performance. We propose an iterative alternating-directions algorithm for minimizing the WLS criterion with respect to a general (not necessarily orthogonal) diagonalizing matrix. Under some mild assumptions, we prove weak convergence in the sense that the norm of parameters update is guaranteed to fall below any arbitrarily small threshold within a finite number of iterations. We distinguish between Hermitian and symmetrical problems. Using BSS simulations results, we demonstrate the improvement in estimating the mixing matrix, resulting from the relaxation of the orthogonality restriction.

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TL;DR: The net result is a new transceiver that is not only computationally efficient compared with the optimal maximum likelihood decoder, but it also yields a probability of error performance that is orders of magnitude smaller than traditional BLAST schemes for the same operating conditions.

Abstract: Turbo-BLAST is a novel multitransmit multireceive (MTMR) antenna scheme for high-throughput wireless communications. It exploits the following ideas: the Bell Labs layered space time (BLAST) architecture; random layered space-time (RLST) coding scheme by using independent block codes and random space-time interleaving; sub-optimal turbo-like receiver that performs iterative decoding of the RLST codes and estimation of the channel matrix in an iterative and, most important, simple fashion. The net result is a new transceiver that is not only computationally efficient compared with the optimal maximum likelihood decoder, but it also yields a probability of error performance that is orders of magnitude smaller than traditional BLAST schemes for the same operating conditions. This paper also presents experimental results using real-life indoor channel measurements demonstrating the high-spectral efficiency of turbo-BLAST.

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TL;DR: The classical particle filter is extended here to the estimation of multiple state processes given realizations of several kinds of observation processes, and the ability of the particle filter to mix different types of observations is made use of.

Abstract: The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking.

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TL;DR: Novel space-time-frequency coding for multi-antenna orthogonal frequency-division multiplexing (OFDM) transmissions over frequency-selective Rayleigh fading channels is proposed and shown to be capable of achieving maximum diversity and coding gains, while affording low-complexity decoding.

Abstract: This paper proposes novel space-time-frequency (STF) coding for multi-antenna orthogonal frequency-division multiplexing (OFDM) transmissions over frequency-selective Rayleigh fading channels. Incorporating subchannel grouping and choosing appropriate system parameters, we first convert our system into a set of group STF (GSTF) systems. This enables simplification of STF coding within each GSTF system. We derive design criteria for STF coding and exploit existing ST coding techniques to construct both STF block and trellis codes. The resulting codes are shown to be capable of achieving maximum diversity and coding gains, while affording low-complexity decoding. The performance merits of our design are confirmed by corroborating simulations and compared with existing alternatives.

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TL;DR: Simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.

Abstract: A generalized singular value decomposition (GSVD) based algorithm is proposed for enhancing multimicrophone speech signals degraded by additive colored noise. This GSVD-based multimicrophone algorithm can be considered to be an extension of the single-microphone signal subspace algorithms for enhancing noisy speech signals and amounts to a specific optimal filtering problem when the desired response signal cannot be observed. The optimal filter can be written as a function of the generalized singular vectors and singular values of a speech and noise data matrix. A number of symmetry properties are derived for the single-microphone and multimicrophone optimal filter, which are valid for the white noise case as well as for the colored noise case. In addition, the averaging step of some single-microphone signal subspace algorithms is examined, leading to the conclusion that this averaging operation is unnecessary and even suboptimal. For simple situations, where we consider localized sources and no multipath propagation, the GSVD-based optimal filtering technique exhibits the spatial directivity pattern of a beamformer. When comparing the noise reduction performance for realistic situations, simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.

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TL;DR: Simulation results indicate that the BER performance of the SDR-ML detector is better than that of these existing detectors and is close to that of the true ML detector, even when the cross-correlations between users are strong or the near-far effect is significant.

Abstract: The maximum-likelihood (ML) multiuser detector is well known to exhibit better bit-error-rate (BER) performance than many other multiuser detectors. Unfortunately,ML detection (MLD) is a nondeterministic polynomial-time hard (NP-hard) problem, for which there is no known algorithm that can find the optimal solution with polynomial-time complexity (in the number of users). In this paper, a polynomial-time approximation method called semi-definite (SD) relaxation is applied to the MLD problem with antipodal data transmission. SD relaxation is an accurate approximation method for certain NP-hard problems. The SD relaxation ML (SDR-ML) detector is efficient in that its complexity is of the order of K3.5, where K is the number of users. We illustrate the potential of the SDR-ML detector by showing that some existing detectors, such as the decorrelator and the linear-minimum-mean-square-error detector, can be interpreted as degenerate forms of the SDR-ML detector. Simulation results indicate that the BER performance of the SDR-ML detector is better than that of these existing detectors and is close to that of the true ML detector, even when the cross-correlations between users are strong or the near-far effect is significant.

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TL;DR: An architecture that performs the forward and inverse discrete wavelet transform (DWT) using a lifting-based scheme for the set of seven filters proposed in JPEG2000 using an architecture consisting of two row processors, two column processors, and two memory modules.

Abstract: We propose an architecture that performs the forward and inverse discrete wavelet transform (DWT) using a lifting-based scheme for the set of seven filters proposed in JPEG2000. The architecture consists of two row processors, two column processors, and two memory modules. Each processor contains two adders, one multiplier, and one shifter. The precision of the multipliers and adders has been determined using extensive simulation. Each memory module consists of four banks in order to support the high computational bandwidth. The architecture has been designed to generate an output every cycle for the JPEG2000 default filters. The schedules have been generated by hand and the corresponding timings listed. Finally, the architecture has been implemented in behavioral VHDL. The estimated area of the proposed architecture in 0.18-/spl mu/ technology is 2.8 nun square, and the estimated frequency of operation is 200 MHz.

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TL;DR: This paper presents a new linear dispersion code design for MIMO Rayleigh fading channels that bridges the gap between multiplexing and diversity and yields codes that typically perform well both in terms of ergodic capacity as well as error probability.

Abstract: Multiple-input multiple-output (MIMO) wireless communication systems provide high capacity due to the plurality of modes available in the channel. Existing signaling techniques for MIMO systems have focused primarily on multiplexing for high data rate or diversity for high link reliability. In this paper, we present a new linear dispersion code design for MIMO Rayleigh fading channels. The proposed design bridges the gap between multiplexing and diversity and yields codes that typically perform well both in terms of ergodic capacity as well as error probability. This is important because, as we show, designs performing well from an ergodic capacity point of view do not necessarily perform well from an error probability point of view. Various techniques are presented for finding codes with good error probability performance. Monte Carlo simulations illustrate performance of some example code designs in terms of ergodic capacity, codeword error probability, and bit error probability.

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TL;DR: It is shown that the global minimum of this nonparametric estimator for Renyi's entropy is the same as the actual entropy, and the performance of the error-entropy-minimization criterion is compared with mean-square-error- Minimization in the short-term prediction of a chaotic time series and in nonlinear system identification.

Abstract: The paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error's Renyi (1970) entropy of order /spl alpha/ and minimization of a Csiszar (1981) distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi's entropy is presented, and it is shown that the global minimum of this estimator is the same as the actual entropy. The performance of the error-entropy-minimization criterion is compared with mean-square-error-minimization in the short-term prediction of a chaotic time series and in nonlinear system identification.

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TL;DR: Identifiability results are provided, showing that in the (theoretical) situation where channel zeros are located on subcarriers, the algorithm does not ensure uniqueness of the channel estimation, unless the full noise subspace is considered.

Abstract: This paper proposes a new blind channel estimation method for orthogonal frequency division multiplexing (OFDM) systems. The algorithm makes use of the redundancy introduced by the cyclic prefix to identify the channel based on a subspace approach. Thus, the proposed method does not require any modification of the transmitter and applies to most existing OFDM systems. Semi-blind procedures taking advantage of training data are also proposed. These can be training symbols or pilot tones, the latter being used for solving the intrinsic indetermination of blind channel estimation. Identifiability results are provided, showing that in the (theoretical) situation where channel zeros are located on subcarriers, the algorithm does not ensure uniqueness of the channel estimation, unless the full noise subspace is considered. Simulations comparing the proposed method with a decision-directed channel estimator finally illustrates the performance of the proposed algorithm.

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TL;DR: Design procedures, based on spectral factorization, for the design of pairs of dyadic wavelet bases where the two wavelets form an approximate Hilbert transform pair are described.

Abstract: Several authors have demonstrated that significant improvements can be obtained in wavelet-based signal processing by utilizing a pair of wavelet transforms where the wavelets form a Hilbert transform pair. This paper describes design procedures, based on spectral factorization, for the design of pairs of dyadic wavelet bases where the two wavelets form an approximate Hilbert transform pair. Both orthogonal and biorthogonal FIR solutions are presented, as well as IIR solutions. In each case, the solution depends on an allpass filter having a flat delay response. The design procedure allows for an arbitrary number of vanishing wavelet moments to be specified. A Matlab program for the procedure is given, and examples are also given to illustrate the results.

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TL;DR: Estimates for the uncoded average symbol error rate of spatial multiplexing and transmit diversity are derived and channel conditions where the use of polarization diversity yields performance improvements are identified.

Abstract: Multiple-input multiple-output (MIMO) antenna systems employ spatial multiplexing to increase spectral efficiency or transmit diversity to improve link reliability. The performance of these signaling strategies is highly dependent on MIMO channel characteristics, which, in turn, depend on antenna height and spacing and richness of scattering. In practice, large antenna spacings are often required to achieve significant multiplexing or diversity gain. The use of dual-polarized antennas (polarization diversity) is a promising cost- and space-effective alternative, where two spatially separated uni-polarized antennas are replaced by a single antenna structure employing orthogonal polarizations. This paper investigates the performance of spatial multiplexing and transmit diversity (Alamouti (see IEEE J. Select. Areas Commun., vol.16, p.1451-58, Oct. 1998) scheme) in MIMO wireless systems employing dual-polarized antennas. In particular, we derive estimates for the uncoded average symbol error rate of spatial multiplexing and transmit diversity and identify channel conditions where the use of polarization diversity yields performance improvements. We show that while improvements in terms of symbol error rate of up to an order of magnitude are possible in the case of spatial multiplexing, the presence of polarization diversity generally incurs a performance loss for transmit diversity techniques. Finally, we provide simulation results to demonstrate that our estimates closely match the actual symbol error rates.

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TL;DR: It is shown that a proper initialization of the recursive procedure leads to an adaptive NMF with the constant false alarm rate (CFAR) property and that it is very effective to operate in heterogeneous environments of relevant practical interest.

Abstract: Adaptive detection of signals embedded in Gaussian or non-Gaussian noise is a problem of primary concern among radar engineers. We propose a recursive algorithm to estimate the structure of the covariance matrix of either a set of Gaussian vectors that share the spectral properties up to a multiplicative factor or a set of spherically invariant random vectors (SIRVs) with the same covariance matrix and possibly correlated texture components. We also assess the performance of an adaptive implementation of the normalized matched filter (NMF), relying on the newly introduced estimate, in the presence of compound-Gaussian, clutter-dominated, disturbance. In particular, it is shown that a proper initialization of the recursive procedure leads to an adaptive NMF with the constant false alarm rate (CFAR) property and that it is very effective to operate in heterogeneous environments of relevant practical interest.