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Signal Processing, IEEE Transactions on

Issue 6 • Date Jun 2002

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Displaying Results 1 - 23 of 23
  • Determination of number of sources with multiple arrays in correlated noise fields

    Page(s): 1257 - 1260
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    A detection scheme for signals in a noise field is considered. The information-theoretic criterion is derived, and it gives a consistent estimation of the number of signals. Comparison of this multiarray estimation and those of the two-array systems is given through simulations View full abstract»

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  • A blind multichannel identification algorithm robust to order overestimation

    Page(s): 1449 - 1458
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (399 KB)  

    Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations View full abstract»

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  • Blind adaptive space-time multiuser detection with multiple transmitter and receiver antennas

    Page(s): 1261 - 1276
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (589 KB)  

    The demand for performance and capacity in cellular systems has generated a great deal of interest in the development of advanced signal processing techniques to optimize the use of system resources. In particular, much work has been done on space-time processing in which multiple transmit/receive antennas are used in conjunction with coding to exploit spatial diversity. We consider space-time multiuser detection using multiple transmit and receive antennas for code-division multiple-access (CDMA) communications. We compare, via analytical bit-error-probability calculations, user capacity, and complexity, two linear receiver structures for different antenna configurations. Motivated by its appearance in a number of third-generation (3G) wideband CDMA standards, we use the Alamouti (see IEEE J. Select. Areas Commun., vol.16, p.1451-58, Oct. 1998) space-time block code for two-transmit-antenna configurations. We also develop blind adaptive implementations for the two transmit/two receive antenna case for synchronous CDMA in flat-fading channels and for asynchronous CDMA, in fading multipath channels. Finally, we present simulation results for the blind adaptive implementations View full abstract»

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  • Algorithms for optimal scheduling and management of hidden Markov model sensors

    Page(s): 1382 - 1397
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (532 KB)  

    The author considers a hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy to minimize a cost function of estimation errors and measurement costs. The optimal measurement policy is solved via stochastic dynamic programming. Sensor management issues and suboptimal scheduling algorithms are also presented. A numerical example that deals with the aircraft identification problem is presented View full abstract»

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  • Channel estimation by symmetrical clustering

    Page(s): 1459 - 1469
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (395 KB)  

    A new blind channel estimation algorithm is presented. This algorithm comes from the well-known maximum likelihood estimation approach. However, we intentionally "smooth" the joint probability density function (PDF) of a finite set of observations in order to reduce the computational burden. As a result, we obtain an online clustering algorithm whose main characteristic is the constraint of symmetry among cluster centers. Computational simulations are used to evaluate this algorithm View full abstract»

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  • Integrated voice/data call admission control for wireless DS-CDMA systems

    Page(s): 1483 - 1495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (437 KB)  

    This paper addresses the call admission control problem for multiservice wireless code division multiple access (CDMA) cellular systems when the physical layer channel and receiver structure at the base station are taken into account. The call admission problem is formulated as a semi-Markov decision process with constraints on the blocking probabilities and signal-to-interference ratio (SIR). By using previous results in large random matrices, the SIR constraints incorporate linear multiuser detectors and fading channels. We show that the optimal call admission policy can be computed via a linear programming-based algorithm View full abstract»

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  • Modulated, perfect reconstruction filterbanks with integer coefficients

    Page(s): 1398 - 1408
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB)  

    We present design methods for perfect reconstruction (PR) integer-modulated filterbanks, including biorthogonal (low-delay) filterbanks. Both the prototype filter and the modulation sequences are composed of integers, thus allowing efficient hardware implementations and fast computation. To derive such filterbanks, we first start with the PR conditions known for cosine modulation and extend them to more general, integer modulation schemes. For the design of biorthogonal PR integer prototypes, a lifting strategy is introduced. To find suitable integer modulation schemes, new algebraic methods are presented. We show solutions where the PR conditions on the prototype filters and the modulation matrices are entirely decoupled and where some simple coupling is introduced. Both even and odd numbers of channels are considered. Design examples are presented for both cases View full abstract»

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  • Fast algorithms of multidimensional discrete nonseparable 𝒦-wave transforms

    Page(s): 1496 - 1507
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    Fast algorithms for a wide class of nonseparable n-dimensional (n-D) discrete unitary 𝒦 transforms (DKTs) are introduced. They need fewer 1-D DKTs than in the case of the classical radix-2 FFT-type approach. The method utilizes a decomposition of the n-D K transform into the product of a new n-D discrete Radon transform and of a set of parallel/independ 1-D K transforms. If the n-D K transform has a separable kernel (e.g., the case of the discrete Fourier transform), our approach leads to decrease of multiplicative complexity by the factor of n, compared with the classical row/column separable approach View full abstract»

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  • A fast refinement for adaptive Gaussian chirplet decomposition

    Page(s): 1298 - 1306
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (383 KB)  

    The chirp function is one of the most fundamental functions in nature. Many natural events, for example, most signals encountered in seismology and the signals in radar systems, can be modeled as the superposition of short-lived chirp functions. Hence, the chirp-based signal representation, such as the Gaussian chirplet decomposition, has been an active research area in the field of signal processing. A main challenge of the Gaussian chirplet decomposition is that Gaussian chirplets do not form an orthogonal basis. A promising solution is to employ adaptive type signal decomposition schemes, such as the matching pursuit. The general underlying theory of the matching pursuit method has been well accepted, but the numerical implementation, in terms of computational speed and accuracy, of the adaptive Gaussian chirplet decomposition remains an open research topic. We present a fast refinement algorithm to search for optimal Gaussian chirplets. With a coarse dictionary, the resulting adaptive Gaussian chirplet decomposition is not only fast but is also more accurate than other known adaptive schemes. The effectiveness of the algorithm introduced is demonstrated by numerical simulations View full abstract»

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  • Design of discrete-coefficient FIR filters on loosely connected parallel machines

    Page(s): 1409 - 1416
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (272 KB)  

    This paper presents a new branch-and-bound mixed-integer linear programming-based algorithm for designing discrete-coefficient finite-impulse response (FIR) filters using a cluster of workstations as the computation platform. The discrete coefficient space considered is the sum of signed power-of-two space, but the technique is also applicable to other discrete coefficient spaces. The key issue determining the success of the algorithm is the ability to partition the original problem into several independent parts that can be distributed to a cluster of machines for solution. The master-slave model is adopted for the control of the machines. Test run results showed that super linear speedup (i.e., the speedup factor is more than the number of machines running in parallel) may be achieved View full abstract»

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  • Statistical analysis of neural network modeling and identification of nonlinear systems with memory

    Page(s): 1508 - 1517
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (451 KB)  

    The paper presents a statistical analysis of neural network modeling and identification of nonlinear systems with memory. The nonlinear system model is comprised of a discrete-time linear filter H followed by a zero-memory nonlinear function g(.). The system is corrupted by input and output independent Gaussian noise. The neural network is used to identify and model the unknown linear filter H and the unknown nonlinearity g(.). The network architecture is composed of a linear adaptive filter and a two-layer nonlinear neural network (with an arbitrary number of neurons). The network is trained using the backpropagation algorithm. The paper studies the MSE surface and the stationary points of the adaptive system. Recursions are derived for the mean transient behavior of the adaptive filter coefficients and neural network weights for slow learning. It is shown that the Wiener solution for the adaptive filter is a scaled version of the unknown filter H. Computer simulations show good agreement between theory and Monte Carlo estimations View full abstract»

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  • Dynamic signal enumeration algorithm for smart antennas

    Page(s): 1307 - 1314
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (330 KB)  

    This paper develops a method to enumerate the incident signals impinging on a uniform linear array (ULA) independent of the extent of their correlation in a Rayleigh flat fading channel environment. The method also minimizes the number of antennas to the number of signals and adapts continuously to maintain performance in a mobile environment where users (signals) come and go. It ensures that the amount of computation is kept to a minimum and, in a practical system, can reallocate computational resources to other applications. The technique is a modification of the matrix decomposition method of Cozzens and Sousa (1994). A new set of stability, stopping, and adaptive control criteria is presented. An algorithm is formulated, and simulation results are presented, showing performance and behavior at various signal-to-noise ratios (SNRs). Limitations of the algorithm in enumerating closely spaced signals are also presented View full abstract»

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  • Efficient algorithms for computing the 2-D hexagonal Fourier transforms

    Page(s): 1438 - 1448
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (491 KB)  

    In this paper, representations of the two-dimensional (2-D) signals are presented that reduce the computation of 2-D discrete hexagonal Fourier transforms (2-D DHFTs). The representations are based on the concept of the covering that reveals the mathematical structure of the transforms. Specifically, a set of unitary paired transforms is derived that splits the 2-D DHFT into a number of smaller one-dimensional (1-D) DFTs. Examples for the 8×4 and 16×8 hexagonal lattices are described in detail. The number of multiplications required for computing the 8×4- and 16×8-point DHFTs are equal 20 and 136, respectively. In the general N⩾8 case, the number of multiplications required to compute the 2N×N-point DHFT by the paired transforms equals N2 (log N-1)+N View full abstract»

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  • Contour-shape based reconstruction of fragmented, 1600 BC wall paintings

    Page(s): 1277 - 1288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (434 KB)  

    A novel general methodology is introduced for the computer-aided reconstruction of the magnificent wall paintings of the Greek island Thera (Santorini), which were painted in the middle of the second millennium BC. These wall paintings have been excavated in fragments, and as a result, their reconstruction is a painstaking and a time-consuming process. Therefore, in order to facilitate and expedite this process, a proper system has been developed based on the introduced methodology. According to this methodology, each fragment is photographed, its picture is introduced to the computer, its contour is obtained, and, subsequently, all of the fragments contours are compared in a manner proposed herein. Both the system and the methodology presented here extract the maximum possible information from the contour shape of fragments of an arbitrary initially unbroken plane object to point out possible fragment matching. This methodology has been applied to two excavated fragmented wall paintings consisting of 262 fragments with full success, but most important, it has been used to reconstruct, for the first time, unpublished parts of wall paintings from a set of 936 fragments View full abstract»

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  • Inherent limitations in data-aided time synchronization of continuous phase-modulation signals over time-selective fading channels

    Page(s): 1470 - 1482
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    Time synchronization of continuous phase modulation (CPM) signals over time selective, Rayleigh fading channels is considered. The Cramer-Rao lower bound (CRLB) for this problem is studied for data-aided (DA) synchronization (i.e., known symbol sequence transmission) over a time-selective Rayleigh fading (i.e., Gaussian multiplicative noise) channel. Exact expressions for the bound are derived as well as simplified, approximate forms that enable derivation of a number of properties that describe the bound's dependence on key parameters such as signal-to-noise ratio (SNR), channel correlation, sampling rate, sequence length, and sequence choice. Comparison with the well-known slow fading (i.e., constant) channel bound is emphasized. Further simplifications are obtained for the special case of minimum phase keying (MSK), wherein it is shown how the bound may be used as a sequence design tool to optimize synchronization performance View full abstract»

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  • Sampling signals with finite rate of innovation

    Page(s): 1417 - 1428
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB)  

    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 View full abstract»

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  • Blind MAI and ISI suppression for DS/CDMA systems using HOS-based inverse filter criteria

    Page(s): 1368 - 1381
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (525 KB)  

    Cumulant-based inverse filter criteria (IFC) using second-and higher order statistics (HOS) proposed by Tugnait et al. (1993) have been widely used for blind deconvolution of discrete-time multi-input multi-output (MIMO) linear time-invariant systems with non-Gaussian measurements through a multistage successive cancellation procedure, but the deconvolved signals turn out to be an unknown permutation of the driving inputs. A multistage blind equalization algorithm (MBEA) is proposed for multiple access interference (MAI) and intersymbol interference (ISI) suppression of multiuser direct sequence/code division multiple access (DS/CDMA) systems in the presence of multipath. The proposed MBEA, which processes the chip waveform matched filter output signal without requiring any path delay information, includes blind deconvolution processing using IFC followed by identification of the estimated symbol sequence with the associated user through a user identification algorithm (UIA). Then, some simulation results are presented to support the proposed MBEA and UIA. Finally, some conclusions are drawn View full abstract»

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  • Mean square convergence of adaptive envelope-constrained filtering

    Page(s): 1429 - 1437
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    A new type of adaptive filtering scheme for solving an envelope-constrained filter design problem in a stochastic environment has been presented. The steady-state stochastic analysis of the adaptive scheme is established in the sense of the mean square. It is shown that if a given initial point is in a neighborhood of the origin, the adaptive scheme with a fixed step-size produces a filter that converges in the mean square sense to within an upper boundary of the noise-free optimum filter. Numerical examples involving pulse compression and channel equalization are presented to illustrate the convergence characteristics of the adaptive scheme operating in a stochastic environment View full abstract»

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  • A class of adaptive step-size control algorithms for adaptive filters

    Page(s): 1315 - 1326
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    A class of new adaptive step-size control algorithms, which is applicable to most of the LMS-derived tap weight adaptation algorithms, is proposed. Analysis yields a set of difference equations for theoretically calculating the transient behavior of the filter convergence and derives an explicit formula for the steady-state excess mean-square error (EMSE). Experiments for some examples prove that the proposed algorithm is highly effective in improving the convergence rate in both transient and tracking phases. The theoretically calculated convergence is shown to be in good agreement with that obtained through simulations. Alternative formulae of the step-size adaptation for specific tap weight adaptation algorithms are also proposed View full abstract»

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  • Hidden Gauss-Markov models for signal classification

    Page(s): 1355 - 1367
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the Baum (1972), Viterbi (1962), and Baum-Welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss-Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that the Baum and Viterbi algorithms for HGMMs are implemented by two different formulations of the fixed-interval Kalman smoother. The measurement likelihoods obtained from the forward pass of the HGMM Baum algorithm and from the Kalman-filter innovation sequence are shown to be equal. A direct link between the Baum-Welch training algorithm and an existing expectation-maximization (EM) algorithm for Gaussian models is demonstrated. A new expression for the cross covariance between time-adjacent states in HGMMs is derived from the off-diagonal block of the conditional joint covariance matrix. A parameter invariance structure is noted for the HGMM likelihood function. CS-HMMs and HGMMs are extended to incorporate mixture densities for the a priori density of the initial state. Application of HGMMs to signal classification is demonstrated with a three-class test simulation View full abstract»

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  • Amplitude spectrum estimation for two-dimensional gapped data

    Page(s): 1343 - 1354
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    The amplitude and phase estimation (APES) approach to nonparametric spectrum estimation of uniformly sampled data has received considerable interest. We consider the extension of APES to gapped data, i.e., uniformly sampled data with missing samples. It has been shown that the APES estimate of the spectrum is the minimizer of a certain least-squares (LS) criterion, and our extension of APES is based on minimizing this LS criterion with respect to the missing data as well. A computationally efficient method for doing this based on cyclic minimization and the conjugate gradient algorithm is proposed. The new algorithm is called gapped-data APES (GAPES) and is developed for the two-dimensional (2-D) case, with the one-dimensional (1-D) case as a special instance. Numerical examples are provided to demonstrate the performance of the algorithm and to show the advantages of 2-D data processing over 1-D (row or column-wise) data processing, as well as to show the applicability of the algorithm to synthetic aperture radar (SAR) imaging View full abstract»

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  • Stochastic analysis of the filtered-X LMS algorithm in systems with nonlinear secondary paths

    Page(s): 1327 - 1342
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    This paper presents a statistical analysis of the filtered-X LMS algorithm behavior when the secondary path (output of the adaptive filter) includes a nonlinear element. This system is of special interest for active acoustic noise and vibration control, where a saturation nonlinearity models the nonlinear distortion introduced by the power amplifiers and transducers. Deterministic nonlinear recursions are derived for Gaussian inputs for the transient mean weight, mean square error, and cross-covariance matrix of the adaptive weight vector at different times. The cross-covariance results provide improved steady-state predictions (as compared with previous results) for moderate to large step sizes. Monte Carlo simulations show excellent agreement with the behavior predicted by the theoretical models. The analytical and simulation results show that a small nonlinearity can have a significant impact on the adaptive filter behavior View full abstract»

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  • Tomography time-frequency transform

    Page(s): 1289 - 1297
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (425 KB)  

    The paper shows that the fractional Fourier transform (FRFT) of a signal is the Radon transform of the time-frequency distribution of the same signal. Therefore, a time-frequency distribution known as the tomography time-frequency transform (TTFT) is defined as the inverse Radon transform of the FRFT of the signal. Because the computation of the TTFT does not explicitly require any window or kernel function, high resolutions in both the frequency and time domains can be achieved. When the signal contains multiple components, the cross terms can be effectively removed by an adaptive filtering process that is applied on the FRFT rather than the final result. Therefore, distortions made by the filtering process on the desired signal components can be minimized 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