By Topic

# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 25 of 46

Publication Year: 2010, Page(s):C1 - C4
| PDF (131 KB)
• ### IEEE Transactions on Signal Processing publication information

Publication Year: 2010, Page(s): C2
| PDF (39 KB)
• ### Structured Least Squares Problems and Robust Estimators

Publication Year: 2010, Page(s):2453 - 2465
Cited by:  Papers (12)
| | PDF (1124 KB) | HTML

A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establ... View full abstract»

• ### A Nonlinear Method for Robust Spectral Analysis

Publication Year: 2010, Page(s):2466 - 2474
Cited by:  Papers (14)
| | PDF (405 KB) | HTML

A nonlinear spectral analyzer, called the L p-norm periodogram, is obtained by replacing the least-squares criterion with an L p-norm criterion in the regression formulation of the ordinary periodogram. In this paper, we study the statistical properties of the L p-norm periodogram for time series with continuous and mixed spectra. We derive the... View full abstract»

• ### Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation

Publication Year: 2010, Page(s):2475 - 2483
Cited by:  Papers (28)
| | PDF (435 KB) | HTML

The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. The approach can be formulated as an optimization problem whose optimal solution can be derived analytically. However, the following issues must still be resolved: estimating the robustness of the operator's parameters and the Lagrangian multipliers, and determining how much of... View full abstract»

• ### A General Algebraic Algorithm for Blind Extraction of One Source in a MIMO Convolutive Mixture

Publication Year: 2010, Page(s):2484 - 2493
Cited by:  Papers (4)
| | PDF (717 KB) | HTML

The paper deals with the problem of blind source extraction from a multiple-input/multiple-output (MIMO) convolutive mixture. We define a new criterion for source extraction which uses higher-order contrast functions based on so called reference signals. It generalizes existing reference-based contrasts. In order to optimize the new criterion, we propose a general algebraic algorithm based on best... View full abstract»

• ### Correlation and Spectral Methods for Determining Uncertainty in Propagating Discontinuities

Publication Year: 2010, Page(s):2494 - 2508
Cited by:  Papers (2)
| | PDF (1612 KB) | HTML

The accurate determination of the speed of a propagating disturbance is important for a number of applications. A nonstationary cross-spectral density phase (NCSDP) technique was developed to provide a statistical estimate of the propagation time of sharp discontinuities such as steps or spikes that model shock or detonation waves. The uncertainty of the phase estimate is dependent on the coherenc... View full abstract»

• ### Robust Kalman Filter Based on a Generalized Maximum-Likelihood-Type Estimator

Publication Year: 2010, Page(s):2509 - 2520
Cited by:  Papers (46)
| | PDF (720 KB) | HTML

A new robust Kalman filter is proposed that detects and bounds the influence of outliers in a discrete linear system, including those generated by thick-tailed noise distributions such as impulsive noise. Besides outliers induced in the process and observation noises, we consider in this paper a new type called structural outliers. For a filter to be able to counter the effect of these outliers, o... View full abstract»

• ### A Continuous-Time Linear System Identification Method for Slowly Sampled Data

Publication Year: 2010, Page(s):2521 - 2533
Cited by:  Papers (12)
| | PDF (520 KB) | HTML

Both direct and indirect methods exist for identifying continuous-time linear systems. A direct method estimates continuous-time input and output signals from their samples and then use them to obtain a continuous-time model, whereas an indirect method estimates a discrete-time model first. Both methods rely on fast sampling to ensure good accuracy. In this paper, we propose a more direct method w... View full abstract»

• ### Variance-Constrained ${cal H}_{infty}$ Filtering for a Class of Nonlinear Time-Varying Systems With Multiple Missing Measurements: The Finite-Horizon Case

Publication Year: 2010, Page(s):2534 - 2543
Cited by:  Papers (94)
| | PDF (542 KB) | HTML

This paper is concerned with the robust H finite-horizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. All the system parameters are time-varying and the uncertainty enters into the state matrix. The measurement missing phenomenon occurs in a random way, an... View full abstract»

• ### FIR Smoothing of Discrete-Time Polynomial Signals in State Space

Publication Year: 2010, Page(s):2544 - 2555
Cited by:  Papers (24)
| | PDF (1130 KB) | HTML

We address a smoothing finite impulse response (FIR) filtering solution for deterministic discrete-time signals represented in state space with finite-degree polynomials. The optimal smoothing FIR filter is derived in an exact matrix form requiring the initial state and the measurement noise covariance function. The relevant unbiased solution is represented both in the matrix and polynomial forms ... View full abstract»

• ### Systematic Construction of Real Lapped Tight Frame Transforms

Publication Year: 2010, Page(s):2556 - 2567
Cited by:  Papers (3)
| | PDF (945 KB) | HTML

We present a constructive algorithm for the design of real lapped equal-norm tight frame transforms. These transforms can be efficiently implemented through filter banks and have recently been proposed as a redundant counterpart to lapped orthogonal transforms, as well as an infinite-dimensional counterpart to harmonic tight frames. The proposed construction consists of two parts: First, we design... View full abstract»

• ### Short-Time Fractional Fourier Transform and Its Applications

Publication Year: 2010, Page(s):2568 - 2580
Cited by:  Papers (69)
| | PDF (1664 KB) | HTML

The fractional Fourier transform (FRFT) is a potent tool to analyze the chirp signal. However, it fails in locating the fractional Fourier domain (FRFD)-frequency contents which is required in some applications. The short-time fractional Fourier transform (STFRFT) is proposed to solve this problem. It displays the time and FRFD-frequency information jointly in the short-time fractional Fourier dom... View full abstract»

• ### Superposition Frames for Adaptive Time-Frequency Analysis and Fast Reconstruction

Publication Year: 2010, Page(s):2581 - 2596
Cited by:  Papers (16)  |  Patents (1)
| | PDF (1263 KB) | HTML

In this paper, we introduce a broad family of adaptive, linear time-frequency representations termed superposition frames, and show that they admit desirable fast overlap-add reconstruction properties akin to standard short-time Fourier techniques. This approach stands in contrast to many adaptive time-frequency representations in the existing literature, which, while more flexible than standard f... View full abstract»

• ### Noninvertible Gabor Transforms

Publication Year: 2010, Page(s):2597 - 2612
Cited by:  Papers (2)
| | PDF (855 KB) | HTML

Time-frequency analysis, such as the Gabor transform, plays an important role in many signal processing applications. The redundancy of such representations is often directly related to the computational load of any algorithm operating in the transform domain. To reduce complexity, it may be desirable to increase the time and frequency sampling intervals beyond the point where the transform is inv... View full abstract»

• ### Best Basis Compressed Sensing

Publication Year: 2010, Page(s):2613 - 2622
Cited by:  Papers (60)
| | PDF (1044 KB) | HTML

This paper proposes a best basis extension of compressed sensing recovery. Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. A new iterative thresholding algorithm performs both the recovery of the signal and the estimation of the best basis. The resulting r... View full abstract»

• ### Optimal Estimation and Detection in Homogeneous Spaces

Publication Year: 2010, Page(s):2623 - 2635
Cited by:  Papers (2)
| | PDF (845 KB) | HTML

This paper presents estimation and detection techniques in homogeneous spaces that are optimal under the squared error loss function. The data is collected on a manifold which forms a homogeneous space under the transitive action of a compact Lie group. Signal estimation problems are addressed by formulating Wiener-Hopf equations for homogeneous spaces. The coefficient functions of these equations... View full abstract»

• ### Recovering Signals From Lowpass Data

Publication Year: 2010, Page(s):2636 - 2646
Cited by:  Papers (3)  |  Patents (1)
| | PDF (450 KB) | HTML

The problem of recovering a signal from its low frequency components occurs often in practical applications due to the lowpass behavior of many physical systems. Here, we study in detail conditions under which a signal can be determined from its low-frequency content. We focus on signals in shift-invariant spaces generated by multiple generators. For these signals, we derive necessary conditions o... View full abstract»

• ### Some Aspects of Band-Limited Extrapolations

Publication Year: 2010, Page(s):2647 - 2653
| | PDF (1082 KB) | HTML

In this paper, some problems in band-limited extrapolations are discussed. These aspects include the computation of the inverse Fourier transform, the accuracy of the extrapolation, the ill-posedness, and regularization method. View full abstract»

• ### Dithered A/D Conversion of Smooth Non-Bandlimited Signals

Publication Year: 2010, Page(s):2654 - 2666
Cited by:  Papers (5)
| | PDF (583 KB) | HTML

The classical method for sampling a smooth non-bandlimited signal requires a lowpass anti-aliasing filter. In applications like distributed sampling where sampling and quantization operations precede filtering, aliasing-error is inevitable. Motivated by such applications, the sampling of smooth and bounded non-bandlimited signals whose spectra have a finite absolute first moment, without the use o... View full abstract»

• ### A Linear Cost Algorithm to Compute the Discrete Gabor Transform

Publication Year: 2010, Page(s):2667 - 2674
Cited by:  Papers (6)
| | PDF (573 KB) | HTML

In this paper, we propose an alternative efficient method to calculate the Gabor coefficients of a signal given a synthesis window with a support of size much lesser than the length of the signal. The algorithm uses the canonical dual of the window (which does not need to be calculated beforehand) and achieves a computational cost that is linear with the signal length in both analysis and synthesi... View full abstract»

• ### Bayesian Orthogonal Component Analysis for Sparse Representation

Publication Year: 2010, Page(s):2675 - 2685
Cited by:  Papers (32)
| | PDF (1453 KB) | HTML

This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix. This issue is formulated in a Bayesian framework. First, the unknown sparse sources are modeled as Bernoulli-G... View full abstract»

• ### Active Learning and Basis Selection for Kernel-Based Linear Models: A Bayesian Perspective

Publication Year: 2010, Page(s):2686 - 2700
Cited by:  Papers (10)
| | PDF (1085 KB) | HTML

We develop an active learning algorithm for kernel-based linear regression and classification. The proposed greedy algorithm employs a minimum-entropy criterion derived using a Bayesian interpretation of ridge regression. We assume access to a matrix, ? ? BBRN?N, for which the (i,j)th element is defined by the kernel function K(?i,?j) ? BBR, with th... View full abstract»

• ### Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures

Publication Year: 2010, Page(s):2701 - 2714
Cited by:  Papers (15)
| | PDF (789 KB) | HTML

The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure and the parameters of the Gaussian distribution on the learning rate as the number of samples increases is discussed. Specifically, the error exponent corresponding to the event that the estimated tree structure differs f... View full abstract»

• ### Tensor-Based Spatial Smoothing (TB-SS) Using Multiple Snapshots

Publication Year: 2010, Page(s):2715 - 2728
Cited by:  Papers (10)
| | PDF (2383 KB)

Tensor-based spatial smoothing (TB-SS) is a preprocessing technique for subspace-based parameter estimation of damped and undamped harmonics. In TB-SS, multichannel data is packed into a measurement tensor. We propose a tensor-based signal subspace estimation scheme that exploits the multidimensional invariance property exhibited by the highly structured measurement tensor. In the presence of nois... 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