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Signal Processing, IEEE Transactions on

Issue 9 • Date Sep 1994

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Displaying Results 1 - 25 of 37
  • Performance analysis of the UCA-ESPRIT algorithm for circular ring arrays

    Page(s): 2535 - 2539
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB)  

    Statistical performance analysis of the UCA (uniform circular array)-ESPRIT algorithm is the subject of the present paper. The UCA-ESPRIT arrival angle estimates are shown to be asymptotically unbiased, and expressions for the asymptotic estimator variances have been obtained. Simulation results that verify the analysis are presented View full abstract»

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  • Eigenstructure techniques for 2-D angle estimation with uniform circular arrays

    Page(s): 2395 - 2407
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1196 KB)  

    The problem of 2D angle estimation (azimuth and elevation) of multiple plane waves incident on a uniform circular array (UCA) of antennas is considered. Two eigenstructure-based estimation algorithms that operate in beamspace and employ phase mode excitation-based beamformers have been developed. The first, UCA-RB-MUSIC, is a beamspace version of MUSIC that offers numerous advantages over element space operation, including reduced computation, as subspace estimates are obtained via real-valued eigendecompositions, enhanced performance in correlated source scenarios due to the attendant forward-backward averaging effect, and the applicability of Root-MUSIC. The second, UCA-ESPRIT, represents a significant advance in the area of 2D angle estimation. It is a novel closed-form algorithm that provides automatically paired source azimuth and elevation estimates. With UCA-ESPRIT, the eigenvalues of a matrix have the form μi=sin θiejφ(i/), where θsub i/ and φi are the elevation and azimuth angles, respectively. Expensive search procedures being thus avoided, UCA-ESPRIT is superior to existing 2D angle estimation algorithms with respect to computational complexity. Finally, asymptotic expressions for the variances of the element space MUSIC and UCA-RB-MUSIC estimators for the 2D scenario have been derived. Results of simulations that compare UCA-RB-MUSIC and UCA-ESPRIT and also validate the theoretical performance expressions are presented View full abstract»

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  • Parameter estimation of cyclostationary AM time series with application to missing observations

    Page(s): 2408 - 2419
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (988 KB)  

    Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in the paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results View full abstract»

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  • Two-dimensional block diagonal LMS adaptive filtering

    Page(s): 2420 - 2429
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB)  

    The paper is concerned with the development of two-dimensional (2D) adaptive filters using the block diagonal least mean squared (BDLMS) method. In this adaptive filtering scheme, the image is scanned and processed block by block in a diagonal fashion, and the filter weights are adjusted once per block rather than once per pixel. The diagonal scanning is adopted to avoid the problems inherent in the 1D standard scanning schemes and to account for the correlations in two directions. The weight updating equation for 2D BDLMS is derived, and the convergence properties of the algorithms are investigated. Simulation results that indicate the effectiveness of the 2D BDLMS when used for image enhancement, estimation, and detection applications are presented View full abstract»

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  • An eigenvector technique for detecting the number of emitters in a cluster

    Page(s): 2380 - 2388
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB)  

    The paper introduces a new algorithm for estimating the number of sources in a cluster of closely spaced sources. The algorithm is based on consideration of the eigenvectors of the sample covariance matrix and is designated as the eigenvector detection technique (EDT). It is shown by examples that the EDT can reliably detect sources that number at lower signal-to-noise ratios (SNRs) than either the minimum description length (MDL) or Akaike information criterion (AIC) algorithms. The paper also presents a performance analysis of the EDT. Results include a “theoretical” expression for detection threshold SNR and a “theoretical” curve of probability of detection versus SNR for the technique; all analysis results show good agreement with simulation results View full abstract»

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  • Some novel multirate architectures for filter realization with reduced multiplicative complexity

    Page(s): 2492 - 2495
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    Presents some novel multirate architectures based on multirate subsystems in feedback configuration for the realization of recursive (AR) filters with reduced multiplicative complexity. Included in this are composite architectures in which there are multiple multirate subsystems with different multirate factors. This can be extended to ARMA transfer functions in which multirate subsystems occur both in feedback and feedforward configuration. The tradeoffs between additive and multiplicative complexity in such architectures are discussed. A summary of results on the effects of coefficient perturbations in such architectures due to finite word length is presented. For exact analysis of such perturbed composite multirate systems, it is shown that these systems can be equivalently represented by a MIMO multirate system View full abstract»

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  • Polynomial perceptrons and their applications to fading channel equalization and co-channel interference suppression

    Page(s): 2470 - 2480
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    This paper investigates the behaviors of polynomial perceptrons and introduces a fractionally spaced recursive polynomial perceptron with low complexity and fast convergence rate. The nonlinear mapping ability of the polynomial perceptron is analyzed. It is shown that a polynomial perceptron with degree L(⩾4) satisfies the Stone-Weierstrass theorem and can approximate any continuous function to within a specified accuracy. Moreover, the nonlinear mapping ability of a polynomial perceptron with degree L is similar to that of the three-layer perceptron with one hidden layer for time same number of neurons in the input layer. The nonlinear mapping ability of the fractionally spaced recursive polynomial perceptron is also presented. Applications of polynomial perceptrons for fading channel equalization and co-channel interference suppression in a 16-level quadrature amplitude modulation receiver system are considered. Computer simulations are used to evaluate and compare the performance of polynomial perceptron (PP) and fractionally spaced bilinear perceptron (FSBLP) with that of the synchronous decision feedback multilayer perceptron (SDFMLP), fractionally spaced decision feedback multilayer perceptron (FSDFMLP), and the conventional decision feedback equalizer (DFE). The results show that the performance of the fractionally spaced bilinear perceptron is clearly superior to that of the other structures View full abstract»

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  • Linear periodic systems and multirate filter design

    Page(s): 2242 - 2256
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1284 KB)  

    We present a systematic procedure for the design of filters intended for multirate systems. This procedure Is motivated by viewing the equiripple design of filters in linear time-invariant systems as a process of obtaining optimum minimax filters for a class of bounded energy input signals. The philosophy of designing optimum minimax filters for classes of input signals is extended to multirate systems, which are not time-invariant. We develop a generalized Fourier analysis appropriate for linear periodic systems and use it to derive new error criteria for multirate filter design. Using such criteria yields optimum minimax multirate filters for the input signal class. The utility of our method is demonstrated by using it to analyze several multirate systems. We give numerical results on the design of a multirate implementation of a narrowband filter and compare our work to previous work on multirate filter design. Our numerical analysis is based upon a new formulation of the design as a semi-infinite linear programming problem View full abstract»

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  • A new cumulant based parameter estimation method for noncausal autoregressive systems

    Page(s): 2524 - 2527
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (352 KB)  

    Proposes a new nonlinear parameter estimation method for a noncausal autoregressive (AR) system based on a new quadratic equation relating the unknown AR parameters to higher order (⩾3) cumulants of nonGaussian output measurements in the presence of additive Gaussian noise. A gradient-type numerical optimization algorithm is used to search for the optimal AR parameter estimates. It is applicable regardless of whether or not the order of the system is known in advance; it is also applicable for the case of the causal AR system. Some simulation results are offered to justify that the proposed method is effective View full abstract»

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  • A sequential multidimensional Cooley-Tukey algorithm

    Page(s): 2430 - 2438
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (628 KB)  

    This work extends the serial/parallel and parallel/serial sequential FFT algorithms proposed, in the 1-D case by Singleton (1967) to the multidimensional (MD) case. MD sequential FFT can be profitably exploited for processing large data arrays that do not fit easily into the computer memory. Such situations are typical in image sequence analysis (for instance, it is quite common with television sequences). The MD sequential FFT retains the constant geometry characteristics of the Singleton algorithms, which is a feature that is very useful for implementation purposes View full abstract»

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  • On the design of inphase and quadrature filters for delay compensation

    Page(s): 2501 - 2503
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB)  

    This correspondence addresses the problem of simultaneous separation and delay compensation of the inphase and quadrature components of a signal. The proposed approach is based on the notions of combined filter and common filter. The combined filter for a given delay has twice as many coefficients as the inphase filter or the quadrature filter. The coefficients of the combined filter for an arbitrary delay are computed as a linear combination of two members of a set of discrete delay filters. Finally, to further simplify the design, all the discrete delay filters in the above mentioned set are polyphase components of a long filter called the common filter. The length of the common filter is determined by the requirement that all possible linear combinations of adjacent coefficients result in a filter that meets the desired passband and stopband ripple specifications. This method, which requires the design of one single filter, has much less computational complexity than extant approaches which require the design of a filter for every specified delay. The design of a product detector for an agile beamformer motivates the proposed method View full abstract»

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  • On-line subspace algorithms for tracking moving sources

    Page(s): 2319 - 2330
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (916 KB)  

    Proposes a class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors. The proposed methods estimate the DOA using only linear operations on the data, and can hence be implemented in a very efficient manner. Furthermore, these methods can accommodate more general noise models than the spatially white noise model commonly used in the literature. Large sample expressions are derived for the variance of the estimates obtained by using the proposed techniques. A comparative statistical study is performed in which comparisons against MUSIC are considered. It is found that usually MUSIC offers slightly more accurate DOA estimates at the cost of an increased computational burden and a more restrictive noise model. The paper includes simulation results lending support to the theoretical results obtained View full abstract»

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  • An algorithm to separate nonstationary part of a signal using mid-prediction filter

    Page(s): 2276 - 2279
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (300 KB)  

    In the autoregressive model, both end-prediction (prediction using the past N values) and mid-prediction (prediction using N/2 past and N/2 future values) filters may be used. If the nonstationary part consists of random impulsive waves of low occurrence rate, it may be separated as an error signal corrupted with “carried over” errors. The latter is removed using the signal-inversion technique. Impulses with slowly rising and falling edges are sometimes recovered better by the mid-prediction filter because it provides higher gain at low frequencies View full abstract»

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  • Design of equiripple nonrecursive digital differentiators and Hilbert transformers using a weighted least-squares technique

    Page(s): 2504 - 2509
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB)  

    A procedure for the design of equiripple digital differentiators (DDs) and Hilbert transformers (HTs) using a weighted least-squares technique is described. This procedure involves an iterative algorithm in which the appropriate frequency-dependent weighting function that yields an equiripple design is determined. Our procedure is used in conjunction with prediction techniques for the DD and HT lengths to design DDs and HTs, respectively, satisfying prescribed specifications View full abstract»

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  • Second-order statistical analysis of totally weighted subspace fitting methods

    Page(s): 2520 - 2524
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB)  

    We analyze the effects of a limited number of snapshots on a general class of multidimensional signal subspace estimation methods, termed totally weighted subspace fitting (TWSF) methods. This class is an extension of the weighted subspace fitting technique that includes row weighting and column weighting of the signal subspace matrix. We quantify the asymptotic statistical properties of the TWSF method by means of a second-order statistical analysis. The main contribution of this article is that it provides, for the first time, the estimator bias. Some simulation results are included to validate our analytical results View full abstract»

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  • Analysis of the variance threshold of Kay's weighted linear predictor frequency estimator

    Page(s): 2370 - 2379
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB)  

    A theoretical approximation for the variance of Kay's weighted linear predictor frequency estimator is derived. From this expression, an inequality describing the variance threshold of the estimator is found. The window weights are then optimized to improve the variance. Numerical simulations demonstrate that the variance approximations are valid for medium to high signal-to-noise ratios or for large numbers of samples View full abstract»

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  • Multichannel ARMA modeling by least squares circular lattice filtering

    Page(s): 2304 - 2318
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1024 KB)  

    The paper makes an attempt to develop least squares lattice algorithms for the ARMA modeling of a linear, slowly time-varying, multichannel system employing scalar computations only. Using an equivalent scalar, periodic ARMA model and a circular delay operator, the signal set for each channel is defined in terms of circularly delayed input and output vectors corresponding to that channel. The orthogonal projection of each current output vector on the subspace spanned by the corresponding signal set is then computed in a manner that allows independent AR and MA order recursions. The resulting lattice algorithm can be implemented in a parallel architecture employing one processor per channel with the data flowing amongst them in a circular manner. The evaluation of the ARMA parameters from the lattice coefficients follows the usual step-up algorithmic approach but requires, in addition, the circulation of certain variables across the processors since the signal sets become linearly dependent beyond certain stages. The proposed algorithm can also be used to estimate a process from two correlated, multichannel processes adaptively allowing the filter orders for both the processes to be chosen independently of each other. This feature is further exploited for ARMA modeling a given multichannel time series with unknown, white input View full abstract»

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  • Determination of the dimension of a signal subspace from short data records

    Page(s): 2531 - 2535
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB)  

    A nested sequence of constant false alarm rate (CFAR) hypothesis tests is presented for rank determination over short data records. The procedure is based on the interpretation of sum of squares of singular values as energy in a particular subspace. The CFAR thresholds are set up based on distributions derived from matrix perturbation ideas. Expressions for probability of overestimation and underestimation are presented View full abstract»

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  • Fast algorithm for minimum-norm direction-of-arrival estimation

    Page(s): 2389 - 2394
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB)  

    The original minimum-norm direction-of-arrival estimator, proposed by Kumaresan and Tufts, employs the noise-subspace projection matrix, calculated by the eigendecomposition of spatial covariance matrix. The present authors propose a novel noneigenvector fast algorithm, which calculates the required minimum-norm function using the special power basis instead of eigenvector basis. The proposed algorithm provides a substantial saving as compared with computational loads of the eigendecomposition-based minimum-norm algorithm in cases when the number of multiple sources is much lower than the number of array sensors. Some computer simulation results, verifying the high performance and accuracy of the proposed algorithm, are presented View full abstract»

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  • Application of signal subspace algorithms to scattered geometric optics and edge-diffracted signals

    Page(s): 2217 - 2226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (804 KB)  

    This paper addresses a signal processing problem arising from a high-frequency radar scattering problem. Signal subspace algorithms apply to signals which are a sum of sinusoids and are widely studied in this context. We consider the application of existing signal subspace algorithms to signals of a more general type. We analyze the accuracy of model parameters estimated and show that the signal subspace algorithms are in a certain sense robust. Thus these algorithms have potential for solving a signal processing problem arising in conventional radar target identification problems View full abstract»

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  • Acoustic vector-sensor array processing

    Page(s): 2481 - 2491
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    A method is presented for localizing acoustic sources using an array of sensors, the output of each being a vector consisting of the acoustic pressure and acoustic particle velocity. The authors derive a compact expression for the Cramer-Rao bound (CRB) on the estimation errors of the source direction-of-arrival (DOA) parameters in the multi-source multi-vector-sensor model. An explicit expression is found for the mean-square angular error (MSAE) bound for source localization with a single vector sensor. The authors present two simple algorithms for estimating the source DOA with this sensor, along with their statistical performance analyses View full abstract»

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  • Optimality conditions for truncated Laguerre networks

    Page(s): 2528 - 2530
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    Presents optimality conditions for truncated Laguerre networks both for continuous time and for discrete time. Contrary to the current approach, no assumption is made about the whiteness of the power spectrum of the input signal. Curiously, the results obtained have the same form as those published for the more restrictive case where the input signal is an impulse at the time origin (or white noise) View full abstract»

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  • Implementation of linear digital filters based on morphological representation theory

    Page(s): 2264 - 2275
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (940 KB)  

    Recent advances in mathematical morphology have led to a representation theory that covers increasing linear translation invariant (ILTI) filters as a subclass of morphological filters. The representation is based on supremum, minimum, and addition operations, and does not require any multiplications. However, this representation has not been practical because its essence is to evaluate the supremum of a set with an infinite number of elements. Based on the previously developed morphological representation, we present a finite max-min representation for ILTI filters. The new representation does not require the use of multipliers and is practical, in the sense that it consists of a finite number of maximum, minimum, and addition operations. The proposed max-min representation is modified to cover FIR linear shift invariant filters in general. The modified representation requires only one multiplication per output sample and has lower computational complexity than the max-min representation. Other related topics such as duality, roundoff noise, and implementation are also discussed View full abstract»

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  • An architecture for lossy compression of waveforms using piecewise-linear approximation

    Page(s): 2449 - 2454
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (476 KB)  

    Lossy compression schemes are often desirable in many signal processing applications such as the compression of ECG data. This paper presents a relaxation of a provably good algorithm for lossy signal compression, based on the piecewise linear approximation of functions. The algorithm approximates the data within a given tolerance using a piecewise linear function. The paper also describes an architecture suitable for the single-chip implementation of the proposed algorithm. The design consists of control, two multiply/divide units, four adder/subtracter units, and an I/O interface unit. For uniformly sampled data, no division is required, and all operations can be completed in a pipelined manner in at most three cycles per sample point. The corresponding simplified architecture is also presented View full abstract»

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  • Time-frequency signal synthesis with time-frequency extrapolation and don't-care regions

    Page(s): 2513 - 2520
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (596 KB)  

    The application of Wigner distribution (WD)-based signal synthesis to signal separation problems is often adversely affected by WD interference terms. We present a modified signal synthesis method where the use of a masked WD allows the definition of don't-care regions. In the don't-care regions, detrimental interference terms (whose time-frequency location is assumed to be known) are ignored. The synthesis result is calculated using a modified version of the quasi power algorithm previously proposed for smoothed WD's. The new synthesis method is shown to possess a desirable time-frequency extrapolation capability as well as a potential tendency to produce spurious signal components in the don't-care regions. The occurrence of spurious signal components can be avoided by the inclusion of an “energy penalty.” 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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Editor-in-Chief
Zhi-Quan (Tom) Luo
University of Minnesota