By Topic

Signal Processing, IEEE Transactions on

Issue 12 • Date Dec. 2004

Filter Results

Displaying Results 1 - 25 of 25
  • Table of contents

    Page(s): c1 - c4
    Save to Project icon | Request Permissions | PDF file iconPDF (43 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Signal Processing publication information

    Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (36 KB)  
    Freely Available from IEEE
  • Bayesian bounds for matched-field parameter estimation

    Page(s): 3293 - 3305
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (512 KB) |  | HTML iconHTML  

    Matched-field methods concern estimation of source locations and/or ocean environmental parameters by exploiting full wave modeling of acoustic waveguide propagation. Because of the nonlinear parameter-dependence of the signal field, the estimate is subject to ambiguities and the sidelobe contribution often dominates the estimation error below a threshold signal-to-noise ratio (SNR). To study the matched-field performance, three Bayesian lower bounds on mean-square error are developed: the Bayesian Crame´r-Rao bound (BCRB), the Weiss-Weinstein bound (WWB), and the Ziv-Zakai bound (ZZB). Particularly, for a multiple-frequency, multiple-snapshot random signal model, a closed-form minimum probability of error associated with the likelihood ratio test is derived, which facilitates error analysis in a wide scope of applications. Analysis and example simulations demonstrate that 1) unlike the local CRB, the BCRB is not achieved by the maximum likelihood estimate (MLE) even at high SNR if the local performance is not uniform across the prior parameter space; 2) the ZZB gives the closest MLE performance prediction at most SNR levels of practical interest; 3) the ZZB can also be used to determine the necessary number of independent snapshots achieving the asymptotic performance of the MLE at a given SNR; 4) incoherent frequency averaging, which is a popular multitone processing approach, reduces the peak sidelobe error but may not improve the overall performance due to the increased ambiguity baseline; and finally, 5) effects of adding additional parameters (e.g., environmental uncertainty) can be well predicted from the parameter coupling. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Minimum variance linear receivers for multiaccess MIMO wireless systems with space-time block coding

    Page(s): 3306 - 3313
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    We consider the problem of joint space-time decoding and multiaccess interference (MAI) rejection in multiuser multiple-input multiple-output (MIMO) wireless communication systems. We address the case when both the receiver and multiple transmitters are equipped with multiple antennas and when space-time block codes (STBCs) are used to send the data simultaneously from each transmitter to the receiver. A new linear receiver structure is developed to decode the data sent from the transmitter-of-interest while rejecting MAI, self-interference, and noise. The proposed receivers are designed by minimizing the output power subject to constraints that zero-force self-interference and/or preserve a unity gain for all symbols of the transmitter-of-interest. Simulation results show that in multiaccess scenarios, the proposed techniques have substantially lower symbol error rates as compared with the matched filter (MF) receiver, which is equivalent to the maximum likelihood (ML) space-time decoder in the point-to-point MIMO communication case. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A small sample model selection criterion based on Kullback's symmetric divergence

    Page(s): 3314 - 3323
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB) |  | HTML iconHTML  

    The Kullback information criterion (KIC) is a recently developed tool for statistical model selection. KIC serves as an asymptotically unbiased estimator of a variant (within a constant) of the Kullback symmetric divergence, known also as J-divergence between the generating model and the fitted candidate model. In this paper, a bias correction to KIC is derived for linear regression models. The correction is of particular use when the sample size is small or when the number of fitted parameters is a moderate to large fraction of the sample size. For linear regression models, the corrected criterion, called KICc is an exactly unbiased estimator of the variant of the Kullback symmetric divergence, assuming that the true model is correctly specified or overfitted. Furthermore, when applied to polynomial regression and autoregressive time-series modeling, KICc is found to estimate the model order more accurately than any other asymptotically efficient method. Finally, KICc is tested on real data to forecast foreign currency exchange rate; the result is very interesting in comparison to classical techniques. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Spectral analysis of randomly sampled signals: suppression of aliasing and sampler jitter

    Page(s): 3324 - 3334
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    Nonuniform sampling can facilitate digital alias-free signal processing (DASP), i.e., digital signal processing that is not affected by aliasing. This paper presents two DASP approaches for spectrum estimation of continuous-time signals. The proposed algorithms, named the weighted sample (WS) and weighted probability (WP) density functions, respectively, utilize random sampling to suppress aliasing. Both methods produce unbiased estimators of the signal spectrum. To achieve this effect, the computational procedure for each method has been suitably matched with the probability density function characterising the pseudorandom generators of the sampling instants. Both proposed methods are analyzed, and the qualities of the estimators they produce have been compared with each other. Although none of the proposed spectrum estimators is universally better than the other one, it has been shown that in practical cases, the WP estimator produces generally smaller errors than those obtained from WS estimation. A practical limitation of the approaches caused by the sampling-instant jitter is also studied. It has been proven that in the presence of jitter, the theoretically infinite bandwidths of WS and WP signal analyses are limited. The maximum frequency up to which these analyses can be performed is inversely proportional to the size of the jitter. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A step-by-step quasi-Newton algorithm in the frequency domain and its application to adaptive channel equalization

    Page(s): 3335 - 3344
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    In this paper, a new efficient adaptive filtering algorithm belonging to the Quasi-Newton (QN) family is proposed. In the new algorithm, the involved inverse Hessian matrix is approximated by a proper expansion, consisting of powers of a Toeplitz matrix. Due to this formulation, the algorithm can be implemented in the frequency domain (FD) using the fast Fourier transform (FFT). Efficient recursive relations for the frequency domain quantities updated on a step-by-step basis have been derived. The proposed algorithm turns out to be particularly suitable for adaptive channel equalization in wireless burst transmission systems. Based on this approach, new adaptive linear equalization (LE) and decision feedback equalization (DFE) algorithms have been developed. These algorithms enjoy the combined advantages of QN formulation and FD implementation, exhibiting faster convergence rate than their stochastic gradient counterparts and less computational complexity, as compared with other Newton-type algorithms. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analysis of gradient algorithms for TLS-based adaptive IIR filters

    Page(s): 3345 - 3356
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (440 KB) |  | HTML iconHTML  

    Steepest descent gradient algorithms for unbiased equation error adaptive infinite impulse response (IIR) filtering are analyzed collectively for both the total least squares and mixed least squares-total least squares framework. These algorithms have a monic normalization that allows for a direct filtering implementation. We show that the algorithms converge to the desired filter coefficient vector. We achieve the convergence result by analyzing the stability of the equilibrium points and demonstrate that only the desired solution is locally stable. Additionally, we describe a region of initialization under which the algorithm converges to the desired solution. We derive the results using interlacing relationships between the eigenvalues of the data correlation matrices and their respective Schur complements. Finally, we illustrate the performance of these new approaches through simulation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Blind input, initial state, and system identification of SIMO Laguerre systems

    Page(s): 3357 - 3369
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB) |  | HTML iconHTML  

    Laguerre filters have infinite impulse responses (IIRs) but with finite tapped delay-line parameterizations. This paper investigates subspace-based blind identification of Laguerre filter tap coefficients, the internal filter state, and the input, given only noisy observations of the output. This paper deals only with single-input, multiple-output (SIMO) Laguerre models. A state space model for the SIMO Laguerre system is derived from which blind estimation algorithms are developed. As in the finite impulse response (FIR) case, the Laguerre filter taps coefficients can be estimated from the column space of a certain Hankel matrix constructed from noisy output observations, whereas the internal state and input can be estimated from the row space by exploiting state space structure. While not exactly uniquely identifiable, conditions are given for which the tap coefficients, the internal state, and the input can be determined to within a multiplicative scalar factor. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Covariance calculation for floating-point state-space realizations

    Page(s): 3370 - 3377
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    This paper provides a new method for analyzing floating-point roundoff error for digital filters by using "finite signal-to-noise" models whose noise sources have variances proportional to the variance or power of the corrupted signals. With this model, a new expression for output error covariance of floating-point arithmetic is derived the in case of double or extended precision accumulation. The output error covariance shows that the optimal state space realization for floating point is the same as that of the fixed-point case, except for two cases: when the filter has poles extremely close to the unit circle or when final quantization to precisions shorter than single precision is employed. An explicit formula is found for determining the minimum number of mantissa bits for stable realization. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Breaking the feedback loop of a class of ΣΔ A/D converters

    Page(s): 3378 - 3393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1688 KB) |  | HTML iconHTML  

    ΣΔ modulation is the currently successful technique used to perform high resolution analog-to-digital conversion. In spite of its practical success, its theoretical signal analysis has remained limited because a ΣΔ modulator contains of a feedback loop that includes a nonlinear operation, i.e., the amplitude discretization or quantization. The feedback allows us to use oversampling to compensate for the limitations of the quantizer in resolution and in precision, which are typical of analog circuits. However, because of the lack of signal analysis, it is still not clear how much resolution of conversion can be gained as a function of the oversampling. We show that for a large class of ΣΔ modulators, the feedback loop theoretically yields an equivalent feedforward signal flow graph, at least for constant inputs. This is possible thanks to remarkable modulo properties of these modulators. This equivalence can be asymptotically extrapolated to time-varying inputs with increasing oversampling. Although the exact components of the equivalent graph are not currently known in general, the theoretical structure of the feedforward graph is sufficient to point out misconceptions in the current knowledge on the final resolution of an nth-order ΣΔ modulator. Specifically, except when the modulator is "ideal", the global resolution of conversion increases by n bits per octave of oversampling, instead of the currently believed rate of n+(1/2) bits/octave. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • QR factoring to compute the GCD of univariate approximate polynomials

    Page(s): 3394 - 3402
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    We present a stable and practical algorithm that uses QR factors of the Sylvester matrix to compute the greatest common divisor (GCD) of univariate approximate polynomials over R[x] or C[x]. An approximate polynomial is a polynomial with coefficients that are not known with certainty. The algorithm of this paper improves over previously published algorithms by handling the case when common roots are near to or outside the unit circle, by splitting and reversal if necessary. The algorithm has been tested on thousands of examples, including pairs of polynomials of up to degree 1000, and is now distributed as the program QRGCD in the SNAP package of Maple 9. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Size of the dictionary in matching pursuit algorithm

    Page(s): 3403 - 3408
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB) |  | HTML iconHTML  

    The matching pursuit algorithm has been successfully applied in many areas such as data compression and pattern recognition. The performance of matching pursuit is closely related to the selection of the dictionary. In this paper, we propose an algorithm to estimate the optimal dictionary distribution ratio and discuss the decay of the norm of residual signal in matching pursuit when the coefficients are quantized by a uniform scalar quantizer. It is proposed that if the approximation error E and the dimension of the space N are given, the optimal size of the dictionary and the optimal quantizer step should be obtained by minimizing the number of bits required to store the matching pursuit representation of any signal in the space to satisfy the error bound. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Prediction of chaotic time series based on the recurrent predictor neural network

    Page(s): 3409 - 3416
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    Chaos limits predictability so that the long-term prediction of chaotic time series is very difficult. The main purpose of this paper is to study a new methodology to model and predict chaotic time series based on a new recurrent predictor neural network (RPNN). This method realizes long-term prediction by making accurate multistep predictions. This RPNN consists of nonlinearly operated nodes whose outputs are only connected with the inputs of themselves and the latter nodes. The connections may contain multiple branches with time delays. An extended algorithm of self-adaptive back-propagation through time (BPTT) learning algorithm is used to train the RPNN. In simulation, two performance measures [root-mean-square error (RMSE) and prediction accuracy (PA)] show that the proposed method is more effective and accurate for multistep prediction. It can identify the systems characteristics quite well and provide a new way to make long-term prediction of the chaotic time series. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analysis of the linear SIC for DS/CDMA signals with random spreading

    Page(s): 3417 - 3428
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB) |  | HTML iconHTML  

    The linear successive interference canceler (LSIC) is a multiuser detector that employs the magnitude of the matched filter (MF) output as the received amplitude estimate of the detected user for use in signal reconstruction. This paper investigates the performance of the LSIC when random spreading sequences are employed. Specifically, the conditional mean and the signal-to-interference-plus-noise ratio (SINR) of the decision variable in each stage are derived to quantify the effects due to imperfect symbol and amplitude estimates. In addition, under the constraint that each user must achieve a certain SINR requirement, we examine the received powers needed for each of a specified number of users and the maximum number of users that a system can support when the LSIC is used in a base station. Computer simulations are presented to compare these results with those of several linear multiuser detectors. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • OFDM channel estimation in the presence of interference

    Page(s): 3429 - 3439
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB) |  | HTML iconHTML  

    We develop a frequency-domain channel estimation algorithm for single-user multiantenna orthogonal frequency division multiplexing (OFDM) wireless systems in the presence of synchronous interference. In this case, the synchronous interferer's signal on each OFDM subcarrier is correlated in space with a rank one spatial covariance matrix. In addition, the interferer's spatial covariance matrix is correlated in frequency based on the delay spread of the interferer's channel. To reduce the number of unknown parameters we develop a structured covariance model that accounts for the structure resulting from the synchronous interference. To further reduce the number of unknown parameters, we model the covariance matrix using a priori known set of frequency-dependent functions of joint (global) parameters. We estimate the interference covariance parameters using a residual method of moments (RMM) estimator and the channel parameters by maximum likelihood (ML) estimation. Since our RMM estimates are invariant to the mean, this approach yields simple noniterative estimates of the covariance parameters while having optimal statistical efficiency. Hence, our algorithm outperforms existing channel estimators that do not account for the interference, and at the same time requires smaller number of pilots than the MANOVA method and thus has smaller overhead. Numerical results illustrate the applicability of the proposed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal transmission strategies and impact of correlation in multiantenna systems with different types of channel state information

    Page(s): 3440 - 3453
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    We study the optimal transmission strategy of a multiple-input single-output (MISO) wireless communication link. The receiver has perfect channel state information (CSI), while the transmitter has different types of CSI, i.e., either perfect CSI, or no CSI, or long-term knowledge of the channel covariance matrix. For the case in which the transmitter knows the channel covariance matrix, it was recently shown that the optimal eigenvectors of the transmit covariance matrix correspond with the eigenvectors of the channel covariance matrix. However, the optimal eigenvalues are difficult to compute. We derive a characterization of the optimum power allocation. Furthermore, we apply this result to provide an efficient algorithm which computes the optimum power allocation. In addition to this, we analyze the impact of correlation on the ergodic capacity of the MISO system with different CSI schemes. At first, we justify the belief that equal power allocation is optimal if the transmitter is uninformed and the transmit antennas are correlated. Next, we show that the ergodic capacity with perfect CSI and without CSI at the transmitter is Schur-concave, i.e., the more correlated the transmit antennas are, the less capacity is achievable. In addition, we show that the ergodic capacity with covariance knowledge at the transmitter is Schur-convex with respect to the correlation properties. These results completely characterize the impact of correlation on the ergodic capacity in MISO systems. Furthermore, the capacity loss or gain due to correlation is quantified. For no CSI and perfect CSI at the transmitter, the capacity loss due to correlation is bounded by some small constant, whereas the capacity gain due to correlation grows unbounded with the number of transmit antennas in the case in which transmitter knows the channel covariance matrix. Finally, we illustrate all theoretical results by numerical simulations. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Channel aware decision fusion in wireless sensor networks

    Page(s): 3454 - 3458
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB)  

    Information fusion by utilizing multiple distributed sensors is studied in this work. Extending the classical parallel fusion structure by incorporating the fading channel layer that is omnipresent in wireless sensor networks, we derive the likelihood ratio based fusion rule given fixed local decision devices. This optimum fusion rule, however, requires perfect knowledge of the local decision performance indices as well as the fading channel. To address this issue, two alternative fusion schemes, namely, the maximum ratio combining statistic and a two-stage approach using the Chair-Varshney fusion rule, are proposed that alleviate these requirements and are shown to be the low and high signal-to-noise ratio (SNR) equivalents of the likelihood-based fusion rule. To further robustify the fusion rule and motivated by the maximum ratio combining statistics, we also propose a statistic analogous to an equal gain combiner that requires minimum a priori information. Performance evaluation is performed both analytically and through simulation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A sinusoidal contrast function for the blind separation of statistically independent sources

    Page(s): 3459 - 3463
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB)  

    The authors propose a new solution to the blind separation of sources (BSS) based on statistical independence. In the two-dimensional (2-D) case, we prove that, under the whiteness constraint, the fourth-order moment-based approximation of the marginal entropy (ME) cost function yields a sinusoidal objective function. Therefore, we can minimize it by simply estimating its phase. We prove that this estimator is consistent for any source distribution. In addition, such results are useful for interpreting other algorithms such as the cumulant-based independent component analysis (CuBICA) and the weighted approximate maximum likelihood (WAML) [or weighted estimator (WE)]. Based on the WAML, we provide a general unifying form for several previous approximations to the ME contrast. The bias and the variance of this estimator have been included. Finally, simulations illustrate the good consistency, convergence, and accuracy of the proposed method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improved adaptive ing of coherent interference without spatial smoothing

    Page(s): 3464 - 3469
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    A new scheme for adaptive ing in the presence of coherent interference is proposed that finds the weight vector using a nonsquare correlation matrix without spatial smoothing. Conventional schemes, which use square matrices, correspond to specific cases of the new method. It allows us to increase the dimension of the weight vector such that the ing capability is enhanced. Theoretic performance analysis is made, showing that the output signal-to-interference ratio (SINR) of such a beamformer is proportional to the weight dimension for sidelobe interference. Simulation results confirm this. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • List of Reviewers

    Page(s): 3470 - 1
    Save to Project icon | Request Permissions | PDF file iconPDF (50 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Signal Processing Information for authors

    Page(s): 3477 - 3478
    Save to Project icon | Request Permissions | PDF file iconPDF (44 KB)  
    Freely Available from IEEE
  • 2004 Index

    Page(s): 3479 - 3515
    Save to Project icon | Request Permissions | PDF file iconPDF (375 KB)  
    Freely Available from IEEE
  • 2005 IEEE Signal Processing Society membership application

    Page(s): 3516
    Save to Project icon | Request Permissions | PDF file iconPDF (176 KB)  
    Freely Available from IEEE
  • IEEE Signal Processing Society Information

    Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (30 KB)  
    Freely Available from IEEE

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

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota