# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 25 of 53

Publication Year: 2011, Page(s):C1 - C4
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• ### IEEE Transactions on Signal Processing publication information

Publication Year: 2011, Page(s): C2
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• ### Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term

Publication Year: 2011, Page(s):1333 - 1345
Cited by:  Papers (21)
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Detection of the number of sinusoids embedded in noise is a fundamental problem in statistical signal processing. Most parametric methods minimize the sum of a data fit (likelihood) term and a complexity penalty term. The latter is often derived via information theoretic criteria, such as minimum description length (MDL), or via Bayesian approaches including Bayesian information criterion (BIC) or... View full abstract»

• ### Subspace SNR Maximization: The Constrained Stochastic Matched Filter

Publication Year: 2011, Page(s):1346 - 1355
Cited by:  Papers (3)
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In this paper, we propose a novel approach to perform detection of stochastic signals embedded in an additive random noise. Both signal and noise are considered to be realizations of zero mean random processes of which only second-order statistics are known (their covariance matrices). The method proposed, called constrained stochastic matched filter (CSMF), is an extension of the stochastic match... View full abstract»

• ### Generalized Likelihood Ratios for Testing the Properness of Quaternion Gaussian Vectors

Publication Year: 2011, Page(s):1356 - 1370
Cited by:  Papers (14)
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In a recent paper, the second-order statistical analysis of quaternion random vectors has shown that there exist two different kinds of quaternion widely linear processing, which are associated with the two main types of quaternion properness. In this paper, we consider the problem of determining, from a finite number of independent vector observations, whether a quaternion Gaussian vector is prop... View full abstract»

• ### The Group Lasso for Stable Recovery of Block-Sparse Signal Representations

Publication Year: 2011, Page(s):1371 - 1382
Cited by:  Papers (30)
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Group Lasso is a mixed l1/l2-regularization method for a block-wise sparse model that has attracted a lot of interests in statistics, machine learning, and data mining. This paper establishes the possibility of stably recovering original signals from the noisy data using the adaptive group Lasso with a combination of sufficient block-sparsity and favorable block... View full abstract»

• ### Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods

Publication Year: 2011, Page(s):1383 - 1396
Cited by:  Papers (19)
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This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structur... View full abstract»

• ### Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Publication Year: 2011, Page(s):1397 - 1408
Cited by:  Papers (29)
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Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-ta... View full abstract»

• ### Tracking of Extended Objects and Group Targets Using Random Matrices

Publication Year: 2011, Page(s):1409 - 1420
Cited by:  Papers (94)
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The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Due to limited sensor resolution capabilities, group targets (i.e., a number of closely spaced targets moving in a coordinated fashion) may show a similar detection pattern as extended objects, namely a varying number of detections whose spread is dete... View full abstract»

• ### Channel-Robust Classifiers

Publication Year: 2011, Page(s):1421 - 1434
Cited by:  Papers (5)
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A key assumption underlying traditional supervised learning algorithms is that labeled examples used to train a classifier are drawn i.i.d. from the same distribution as test samples. This assumption is violated when classifying a test sample whose statistics differ from the training samples because the test signal is the output of a noisy linear time-invariant system, e.g., from channel propagati... View full abstract»

• ### IR-UWB Transmitted-Reference Systems With Partial Channel Knowledge: A Receiver Design Based on the Statistical Invariance Principle

Publication Year: 2011, Page(s):1435 - 1448
Cited by:  Papers (9)
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This paper deals with detection algorithms for impulse-radio ultrawideband transmitted-reference systems. For binary modulation schemes, we propose a novel detector that operates on the partial autocorrelations of the received signal as computed over time intervals (bins) of size less than the symbol period. Assuming that the receiver has knowledge of the partial autocorrelations of the channel re... View full abstract»

• ### Adaptive Volterra Filters With Evolutionary Quadratic Kernels Using a Combination Scheme for Memory Control

Publication Year: 2011, Page(s):1449 - 1464
Cited by:  Papers (23)
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This paper proposes a new paradigm for adaptive Volterra filtering using a time-variant size of the quadratic kernel memory in order to optimally identify any unknown transversal second-order nonlinear system. To this end, competing Volterra structures of different sizes are employed in a hierarchical combination scheme so as to find the best configuration of the second-order kernel memory, using ... View full abstract»

• ### Analysis of Spatial and Incremental LMS Processing for Distributed Estimation

Publication Year: 2011, Page(s):1465 - 1480
Cited by:  Papers (54)
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Consider a set of nodes distributed spatially over some region forming a network, where every node takes measurements of an underlying process. The objective is for every node in the network to estimate some parameter of interest from these measurements by cooperating with other nodes. In this work we compare the performance of four adaptive implementations. Two of the implementations are distribu... View full abstract»

• ### Coprimality of Certain Families of Integer Matrices

Publication Year: 2011, Page(s):1481 - 1490
Cited by:  Papers (4)
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Commuting coprime pairs of integer matrices have been of interest in multidimensional multirate systems, and more recently in array processing. In multirate systems they arise, for example, in the design of interchangeable cascades of decimator and expander matrices. In array processing they arise in the construction of dense coarrays from sparse sensors located on a pair of lattices. For the impo... View full abstract»

• ### Multichannel Sampling of Pulse Streams at the Rate of Innovation

Publication Year: 2011, Page(s):1491 - 1504
Cited by:  Papers (73)  |  Patents (1)
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We consider minimal-rate sampling schemes for infinite streams of delayed and weighted versions of a known pulse shape. The minimal sampling rate for these parametric signals is referred to as the rate of innovation and is equal to the number of degrees of freedom per unit time. Although sampling of infinite pulse streams was treated in previous works, either the rate of innovation was not achieve... View full abstract»

• ### Sampling and Recovery of Pulse Streams

Publication Year: 2011, Page(s):1505 - 1517
Cited by:  Papers (25)
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Compressive sensing (CS) is a new technique for the efficient acquisition of signals, images and other data that have a sparse representation in some basis, frame, or dictionary. By sparse we mean that the N-dimensional basis representation has just K <;<; N significant coefficients; in this case, the CS theory maintains that just M = O( K log N) random linear signal measurements will both p... View full abstract»

• ### A Signal Processing Approach to Fourier Analysis of Ranking Data: The Importance of Phase

Publication Year: 2011, Page(s):1518 - 1527
Cited by:  Papers (4)
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Ranking data is a type of data obtained in some elections, in customer surveys, as well as from web search results. Such data may be considered as a type of signal defined on the group of permutations of n objects, denoted Sn. There exists a Fourier transform for Sn obtained from group representation theory, which is well known in the mathematics literature. However, previous ... View full abstract»

• ### Nonfragile $H_{infty}$ Filtering of Continuous-Time Fuzzy Systems

Publication Year: 2011, Page(s):1528 - 1538
Cited by:  Papers (55)
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This paper is concerned with the problem of nonfragile H∞ filtering for a class of continuous-time fuzzy systems. Attention is focused on the design of a filter such that the filtering error system preserves a prescribed H∞ performance, where the filter to be designed is assumed to have gain variations. By using the quadratic Lyapunov function appr... View full abstract»

• ### Performance Bounds and Angular Resolution Limit for the Moving Colocated MIMO Radar

Publication Year: 2011, Page(s):1539 - 1552
Cited by:  Papers (39)
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To identify a target, the moving noncoherent colocated multiple-input multiple-output (MIMO) radar system takes advantage of multiple antennas in transmission and reception which are close in space. In this paper, we study the estimation performance and the resolution limit for this scheme in which each array geometry is described by the sample-variance of the sensor distribution. So, our analysis... View full abstract»

• ### Time Varying Dynamic Bayesian Network for Nonstationary Events Modeling and Online Inference

Publication Year: 2011, Page(s):1553 - 1568
Cited by:  Papers (21)
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This paper presents a novel time varying dynamic Bayesian network (TVDBN) model for the analysis of nonstationary sequences which are of interest in many fields. The changing network structure and parameter in TVDBN are treated as random processes whose values at each time epoch determine a stationary DBN model; this DBN model is then used to specify the distribution of data sequence at the time e... View full abstract»

• ### Bayesian Nonparametric Inference of Switching Dynamic Linear Models

Publication Year: 2011, Page(s):1569 - 1585
Cited by:  Papers (51)
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Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth... View full abstract»

• ### Quaternion ICA From Second-Order Statistics

Publication Year: 2011, Page(s):1586 - 1600
Cited by:  Papers (40)
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This paper addresses the independent component analysis (ICA) of quaternion random vectors. In particular, we focus on the Gaussian case and therefore only consider the quaternion second-order statistics (SOS), which are given by the covariance matrix and three complementary covariance matrices. First, we derive the necessary and sufficient conditions for the identifiability of the quaternion ICA ... View full abstract»

• ### Iterative Robust Minimum Variance Beamforming

Publication Year: 2011, Page(s):1601 - 1611
Cited by:  Papers (46)
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Based on worst-case performance optimization, the recently developed adaptive beamformers utilize the uncertainty set of the desired array steering vector to achieve robustness against steering vector mismatches. In the presence of large steering vector mismatches, the uncertainty set has to expand to accommodate the increased error. This degrades the output signal-to-interference-plus-noise ratio... View full abstract»

• ### Joint TDOA and FDOA Estimation: A Conditional Bound and Its Use for Optimally Weighted Localization

Publication Year: 2011, Page(s):1612 - 1623
Cited by:  Papers (40)
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Modern passive emitter-location systems are often based on joint estimation of the time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) of an unknown signal at two (or more) sensors. Classical derivation of the associated Cramér-Rao bound (CRB) relies on a stochastic, stationary Gaussian signal-model, leading to a diagonal Fisher information matrix with respect to t... View full abstract»

• ### EXIT Chart-Based Power Allocation for Iterative Frequency Domain MIMO Detector

Publication Year: 2011, Page(s):1624 - 1641
Cited by:  Papers (12)
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Transmission power allocation in single-carrier multiple-input multiple-output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean-squared error (MMSE) equalization is considered. A novel framework for transmission power minimization subject to equalizer convergence constraints, referred as convergence constrained power allocation (CCPA) method, is proposed base... 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
Pier Luigi Dragotti
Imperial College London