By Topic

# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 25 of 64

Publication Year: 2012, Page(s):C1 - C4
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• ### IEEE Transactions on Signal Processing publication information

Publication Year: 2012, Page(s): C2
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• ### Multiple Quadrature Kalman Filtering

Publication Year: 2012, Page(s):6125 - 6137
Cited by:  Papers (14)
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Bayesian filtering is a statistical approach that naturally appears in many signal processing problems. Ranging from Kalman filter to particle filters, there is a plethora of alternatives depending on model assumptions. With the exception of very few tractable cases, one has to resort to suboptimal methods due to the inability to analytically compute the Bayesian recursion in general dynamical sys... View full abstract»

• ### Moment Estimation Using a Marginalized Transform

Publication Year: 2012, Page(s):6138 - 6150
Cited by:  Papers (2)
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We present a method for estimating mean and covariance of a transformed Gaussian random variable. The method is based on evaluations of the transforming function and resembles the unscented transform and Gauss-Hermite integration in that respect. The information provided by the evaluations is used in a Bayesian framework to form a posterior description of the parameters in a model of the transform... View full abstract»

• ### Minimax-Optimal Hypothesis Testing With Estimation-Dependent Costs

Publication Year: 2012, Page(s):6151 - 6165
Cited by:  Papers (5)
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This paper introduces a novel framework for hypothesis testing in the presence of unknown parameters. The objective is to decide between two hypotheses, where each one involves unknown parameters that are of interest to be estimated. The existing approaches on detection and estimation place the primary emphasis on the detection part by solving this part optimally and treating the estimation part s... View full abstract»

• ### A Novel Location-Penalized Maximum Likelihood Estimator for Bearing-Only Target Localization

Publication Year: 2012, Page(s):6166 - 6181
Cited by:  Papers (33)
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In this paper, we present a location-penalized maximum likelihood (LPML) estimator for bearing only target localization. We develop a new penalized maximum likelihood cost function by transforming the variables of target position and bearings. The new penalized likelihood function can also be recognized as a posterior distribution under a Bayesian framework by penalizing a prior. We give analysis ... View full abstract»

• ### Geodesic Convexity and Covariance Estimation

Publication Year: 2012, Page(s):6182 - 6189
Cited by:  Papers (26)
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Geodesic convexity is a generalization of classical convexity which guarantees that all local minima of g-convex functions are globally optimal. We consider g-convex functions with positive definite matrix variables, and prove that Kronecker products, and logarithms of determinants are g-convex. We apply these results to two modern covariance estimation problems: robust estimation in scaled Gaussi... View full abstract»

• ### Radar Maneuvering Target Motion Estimation Based on Generalized Radon-Fourier Transform

Publication Year: 2012, Page(s):6190 - 6201
Cited by:  Papers (51)
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The slant range of a radar maneuvering target is usually modeled as a multivariate function in terms of its illumination time and multiple motion parameters. This multivariate range function includes the modulations on both the envelope and the phase of an echo of the coherent radar target and provides the foundation for radar target motion estimation. In this paper, the maximum likelihood estimat... View full abstract»

• ### Generalized Orthogonal Matching Pursuit

Publication Year: 2012, Page(s):6202 - 6216
Cited by:  Papers (89)
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As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals. Our approach, henceforth referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that ... View full abstract»

• ### Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks

Publication Year: 2012, Page(s):6217 - 6234
Cited by:  Papers (111)
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Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In this work, we compare the mean-square performance of two main strategies for distributed estimation over networks: consensus strategies and diffus... View full abstract»

• ### Stochastic Analysis of a Stable Normalized Least Mean Fourth Algorithm for Adaptive Noise Canceling With a White Gaussian Reference

Publication Year: 2012, Page(s):6235 - 6244
Cited by:  Papers (26)
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The least mean fourth (LMF) algorithm has several stability problems. Its stability depends on the variance and distribution type of the adaptive filter input, the noise variance, and the initialization of the filter weights. A global solution to these stability problems was presented recently for a normalized LMF (NLMF) algorithm. Here, a stochastic analysis of the mean-square deviation (MSD) of ... View full abstract»

• ### Fixed-Point Analysis and Parameter Selections of MSR-CORDIC With Applications to FFT Designs

Publication Year: 2012, Page(s):6245 - 6256
Cited by:  Papers (12)
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Mixed-scaling-rotation (MSR) coordinate rotation digital computer (CORDIC) is an attractive approach to synthesizing complex rotators. This paper presents the fixed-point error analysis and parameter selections of MSR-CORDIC with applications to the fast Fourier transform (FFT). First, the fixed-point mean squared error of the MSR-CORDIC is analyzed by considering both the angle approximation erro... View full abstract»

• ### Exact Wavelets on the Ball

Publication Year: 2012, Page(s):6257 - 6269
Cited by:  Papers (12)
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We develop an exact wavelet transform on the three-dimensional ball (i.e. on the solid sphere), which we name the flaglet transform. For this purpose we first construct an exact transform on the radial half-line using damped Laguerre polynomials and develop a corresponding quadrature rule. Combined with the spherical harmonic transform, this approach leads to a sampling theorem on the ball and a n... View full abstract»

• ### Message-Passing De-Quantization With Applications to Compressed Sensing

Publication Year: 2012, Page(s):6270 - 6281
Cited by:  Papers (42)
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Estimation of a vector from quantized linear measurements is a common problem for which simple linear techniques are suboptimal-sometimes greatly so. This paper develops message-passing de-quantization (MPDQ) algorithms for minimum mean-squared error estimation of a random vector from quantized linear measurements, notably allowing the linear expansion to be overcomplete or undercomplete and the s... View full abstract»

• ### Realization of 3-D Separable-Denominator Digital Filters With Low $l_2$ -Sensitivity

Publication Year: 2012, Page(s):6282 - 6293
Cited by:  Papers (1)
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Three-dimensional (3-D) digital filters find applications in a variety of image and video signal processing problems. This paper presents a coefficient-sensitivity analysis for a wide class of 3-D digital filters with separable denominators in local state space that leads to an analytic formulation for sensitivity minimization, and to present two solution techniques for the sensitivity minimizatio... View full abstract»

• ### New Closed Formula for the Univariate Hermite Interpolating Polynomial of Total Degree and its Application in Medical Image Slice Interpolation

Publication Year: 2012, Page(s):6294 - 6304
Cited by:  Papers (2)
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This work investigates the usefulness of univariate Hermite interpolation of the total degree (HTD) for a biomedical signal processing task: slice interpolation in a variety of medical imaging modalities. The HTD is an algebraically demanding interpolation method that utilizes information of the values of the signal to be interpolated at distinct support positions, as well as the values of its der... View full abstract»

• ### Polynomial Smoothing of Time Series With Additive Step Discontinuities

Publication Year: 2012, Page(s):6305 - 6318
Cited by:  Papers (12)
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This paper addresses the problem of estimating simultaneously a local polynomial signal and an approximately piecewise constant signal from a noisy additive mixture. The approach developed in this paper synthesizes the total variation filter and least-square polynomial signal smoothing into a unified problem formulation. The method is based on formulating an l1-norm regularized i... View full abstract»

• ### A Multilevel Iterated-Shrinkage Approach to $l_{1}$ Penalized Least-Squares Minimization

Publication Year: 2012, Page(s):6319 - 6329
Cited by:  Papers (4)
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The area of sparse approximation of signals is drawing tremendous attention in recent years. Typically, sparse solutions of underdetermined linear systems of equations are required. Such solutions are often achieved by minimizing an l1 penalized least squares functional. Various iterative-shrinkage algorithms have recently been developed and are quite effective for handling these... View full abstract»

• ### A Phase-Sensitive Approach to Filtering on the Sphere

Publication Year: 2012, Page(s):6330 - 6339
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This paper examines filtering on a sphere, by first examining the roles of spherical harmonic magnitude and phase. We show that phase is more important than magnitude in determining the structure of a spherical function. We examine the properties of linear phase shifts in the spherical harmonic domain, which suggest a mechanism for constructing finite-impulse-response (FIR) filters. We show that t... View full abstract»

• ### Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning

Publication Year: 2012, Page(s):6340 - 6353
Cited by:  Papers (43)
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We consider the data-driven dictionary learning problem. The goal is to seek an over-complete dictionary from which every training signal can be best approximated by a linear combination of only a few codewords. This task is often achieved by iteratively executing two operations: sparse coding and dictionary update. The focus of this paper is on the dictionary update step, where the dictionary is ... View full abstract»

• ### Structure-Based Bayesian Sparse Reconstruction

Publication Year: 2012, Page(s):6354 - 6367
Cited by:  Papers (11)
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Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many s... View full abstract»

• ### Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models

Publication Year: 2012, Page(s):6368 - 6381
Cited by:  Papers (1)
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Graphical models use graphs to compactly capture stochastic dependencies amongst a collection of random variables. Inference over graphical models corresponds to finding marginal probability distributions given joint probability distributions. In general, this is computationally intractable, which has led to a quest for finding efficient approximate inference algorithms. We propose a framework for... View full abstract»

• ### DOA Estimation Using a Greedy Block Coordinate Descent Algorithm

Publication Year: 2012, Page(s):6382 - 6394
Cited by:  Papers (14)
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This paper presents a novel jointly sparse signal reconstruction algorithm for the DOA estimation problem, aiming to achieve faster convergence rate and better estimation accuracy compared to existing l2,1-norm minimization approaches. The proposed greedy block coordinate descent (GBCD) algorithm shares similarity with the standard block coordinate descent method for l... View full abstract»

• ### Low-Complexity Blind Equalization for OFDM Systems With General Constellations

Publication Year: 2012, Page(s):6395 - 6407
Cited by:  Papers (4)
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This paper proposes a low-complexity algorithm for blind equalization of data in orthogonal frequency division multiplexing (OFDM)-based wireless systems with general constellations. The proposed algorithm is able to recover the transmitted data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statis... View full abstract»

• ### Quantization via Empirical Divergence Maximization

Publication Year: 2012, Page(s):6408 - 6420
Cited by:  Papers (2)
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Empirical divergence maximization (EDM) refers to a recently proposed strategy for estimating f-divergences and likelihood ratio functions. This paper extends the idea to empirical vector quantization where one seeks to empirically derive quantization rules that maximize the Kullback-Leibler divergence between two statistical hypotheses. We analyze the estimator's error convergence rate lev... View full abstract»

## 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
Sergios Theodoridis
University of Athens