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# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 14 of 14
• ### Passive Detection of Correlated Subspace Signals in Two MIMO Channels

Publication Year: 2017, Page(s):5266 - 5280
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In this paper, we consider a two-channel multiple-input multiple-output passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension, but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and... View full abstract»

• ### Guaranteed-Stable Sliding DFT Algorithm With Minimal Computational Requirements

Publication Year: 2017, Page(s):5281 - 5288
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The discrete Fourier transform (DFT) is the most widely used technique for determining the frequency spectra of digital signals. However, in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated, the use of DFT becomes difficult due to its heavy computational burden. This paper proposes an optimal sliding DFT (oSDFT) algorit... View full abstract»

• ### Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

Publication Year: 2017, Page(s):5289 - 5304
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In millimeter-wave (mmWave) systems, antenna architecture limitations make it difficult to apply conventional fully digital precoding techniques but call for low-cost analog radio frequency (RF) and digital baseband hybrid precoding methods. This paper investigates joint RF-baseband hybrid precoding for the downlink of multiuser multiantenna mmWave systems with a limited number of RF chains. Two p... View full abstract»

• ### Model Order Selection Rules for Covariance Structure Classification in Radar

Publication Year: 2017, Page(s):5305 - 5317
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The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The consi... View full abstract»

• ### Independent Resampling Sequential Monte Carlo Algorithms

Publication Year: 2017, Page(s):5318 - 5333
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Sequential Monte Carlo algorithms, or particle filters, are Bayesian filtering algorithms, which propagate in time a discrete and random approximation of the a posteriori distribution of interest. Such algorithms are based on importance sampling with a bootstrap resampling step, which aims at struggling against weight degeneracy. However, in some situations (informative measuremen... View full abstract»

• ### Wideband Multiple Diversity Tensor Array Processing

Publication Year: 2017, Page(s):5334 - 5346
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This paper establishes a tensor model for wideband coherent array processing including multiple physical diversities. A separable coherent focusing operation is proposed as a preprocessing step in order to ensure the multilinearity of the interpolated data. We propose an alternating least squares algorithm to process tensor data, taking into account the noise correlation structure introduced by th... View full abstract»

• ### Nearly Optimal Bounds for Orthogonal Least Squares

Publication Year: 2017, Page(s):5347 - 5356
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In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On one hand, we show that if the sampling matrix $\mathbf {A}$ satisfies the restricted isometry property of order $K + 1$ with isometry constant View full abstract»

• ### On a Registration-Based Approach to Sensor Network Localization

Publication Year: 2017, Page(s):5357 - 5367
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We consider a registration-based approach for localizing sensor networks from range measurements. This is based on the assumption that one can find overlapping cliques spanning the network. That is, for each sensor, one can identify geometric neighbors for which all inter-sensor ranges are known. Such cliques can be efficiently localized using multidimensional scaling. However, si... View full abstract»

• ### An Efficient Combined Bit-Flipping and Stochastic LDPC Decoder Using Improved Probability Tracers

Publication Year: 2017, Page(s):5368 - 5380
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This paper presents an efficient combined bit-flipping (BF) and stochastic low-density parity-check decoder, where a BF decoder is used to achieve a reduction in decoding cycles. A node-wise probability tracer is adopted at each variable node (VN) in order to achieve a BER performance comparable to the normalized min-sum algorithm, where check-to-variable (C2V) messages are used as inputs, rather ... View full abstract»

• ### An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling

Publication Year: 2017, Page(s):5381 - 5392
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A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm in current ERP-based letter by letter typing BCIs typically ... View full abstract»

• ### A Generalization of the Fixed Point Estimate for Packet-Scaled Complex Covariance Matrix Estimation

Publication Year: 2017, Page(s):5393 - 5405
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In this paper, the problem of estimating complex-valued proportional covariance matrices is addressed. The obtained estimate generalizes the fixed point estimate to scaled packets of data, and is hence called the generalized fixed point estimate (GFPE). The statistical properties (bias, consistency, and asymptotical distributions) of the estimate are presented, and verified through simulations. As... View full abstract»

• ### Uncertainty Principles and Sparse Eigenvectors of Graphs

Publication Year: 2017, Page(s):5406 - 5420
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Analysis of signals defined over graphs has been of interest in the recent years. In this regard, many concepts from the classical signal processing theory have been extended to the graph case, including uncertainty principles that study the concentration of a signal on a graph and in its graph Fourier basis (GFB). This paper advances a new way to formulate the uncertainty principle for signals de... View full abstract»

• ### Missing Data Recovery for High-Dimensional Signals With Nonlinear Low-Dimensional Structures

Publication Year: 2017, Page(s):5421 - 5436
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Motivated by missing data recovery in power system monitoring, we study the problem of recovering missing entries of high-dimensional signals that exhibit low-dimensional nonlinear structures. We propose a novel model, termed as “union and sums of subspaces,” to characterize practical nonlinear datasets. In this model, each data point belongs to either one of a few low-dimensional su... View full abstract»

• ### Stationary Point Characterization for a Class of BCA Algorithms

Publication Year: 2017, Page(s):5437 - 5452
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Bounded component analysis (BCA) is a recently introduced approach including independent component analysis as a special case under the assumption of source boundedness. In this paper, we provide a stationary point analysis for the recently proposed instantaneous BCA algorithms that are capable of separating dependent, even correlated as well as independent sources from their mixtures. The station... 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