# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 13 of 13
• ### Recovery of Independent Sparse Sources From Linear Mixtures Using Sparse Bayesian Learning

Publication Year: 2018, Page(s):6332 - 6346
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Classical algorithms for the multiple measurement vector (MMV) problem assume either independent columns for the solution matrix or certain models of correlation among the columns. The correlation structure in the previous MMV formulation does not capture the signals well for some applications like photoplethysmography (PPG) signal extraction where the signals are independent and linearly mixed in... View full abstract»

• ### Detection Theory for Union of Subspaces

Publication Year: 2018, Page(s):6347 - 6362
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The focus of this paper is on detection theory for union of subspaces (UoS). To this end, generalized likelihood ratio tests (GLRTs) are presented for detection of signals conforming to the UoS model and detection of the corresponding “active” subspace. One of the main contributions of this paper is bounds on the performances of these GLRTs in terms of geometry of subspaces under various assumptio... View full abstract»

• ### Exploiting Sparsity in Tight-Dimensional Spaces for Piecewise Continuous Signal Recovery

Publication Year: 2018, Page(s):6363 - 6376
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Recovery of certain piecewise continuous signals from noisy observations has been a major challenge in sciences and engineering. In this paper, in a tight-dimensional representation space, we exploit sparsity hidden in a class of possibly discontinuous signals named finite-dimensional piecewise continuous (FPC) signals. More precisely, we propose a tight-dimensional linear transformation which rev... View full abstract»

• ### Data-Driven Nonparametric Existence and Association Problems

Publication Year: 2018, Page(s):6377 - 6389
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We investigate two closely related nonparametric hypothesis testing problems. In the first problem (i.e., the existence problem), we test whether a testing data stream is generated by one of a set of composite distributions. In the second problem (i.e., the association problem), we test which one of the multiple distributions generates a testing data stream. We assume that some distributions in th... View full abstract»

• ### Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems

Publication Year: 2018, Page(s):6390 - 6403
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The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach—since such channel can be fully characterized by se... View full abstract»

• ### Linear Canonical Matched Filter: Theory, Design, and Applications

Publication Year: 2018, Page(s):6404 - 6417
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The linear canonical transform (LCT) is a multiparameter unitary transform that generalizes a large number of classical transforms with application to signal processing and optics. Many of its fundamental properties are already known; however, little attention has been paid to the design and implementation of matched filters in the LCT domain. The objective of this paper is to design this type of ... View full abstract»

• ### Spatially Controlled Relay Beamforming

Publication Year: 2018, Page(s):6418 - 6433
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We consider the problem of enhancing Quality-of-Service (QoS) in mobile relay beamforming networks, by optimally controlling relay motion, in the presence of a dynamic channel. We assume a time-slotted system, where the relays update their positions before the beginning of each slot. Modeling the wireless channel as a Gaussian spatiotemporal field, we propose a novel 2-stage stochastic programming... View full abstract»

• ### Phase-Continuous Frequency Line Track-Before-Detect of a Tone With Slow Frequency Variation

Publication Year: 2018, Page(s):6434 - 6442
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We consider optimal Bayesian detection of a slowly varying tone of unknown amplitude in situations characterized by very low signal-to-noise ratio (SNR) and a large number of measurements, as found in certain gravitational wave and passive sonar problems. We use a hidden Markov model (HMM) framework but, unlike typical HMM-based frequency line tracking methods, we develop a true track-before-detec... View full abstract»

• ### Discreteness-Aware Approximate Message Passing for Discrete-Valued Vector Reconstruction

Publication Year: 2018, Page(s):6443 - 6457
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This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), is based on the idea of approximate message passing (AMP), which has been originally proposed for compressed sensing. The DAMP algor... View full abstract»

• ### Source Counting and Separation Based on Simplex Analysis

Publication Year: 2018, Page(s):6458 - 6473
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Blind source separation is addressed, using a novel data-driven approach, based on a well-established probabilistic model. The proposed method is specifically designed for separation of multichannel audio mixtures. The algorithm relies on spectral decomposition of the correlation matrix between different time frames. The probabilistic model implies that the column space of the correlation matrix i... View full abstract»

• ### Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein–Uhlenbeck Process

Publication Year: 2018, Page(s):6474 - 6487
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A novel anomaly detection procedure based on the Ornstein–Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity$\boldsymbol{v}_0$. In order to hide this behavior, the vessel switches off its automatic identification sys... View full abstract»

• ### Line Constrained Estimation With Applications to Target Tracking: Exploiting Sparsity and Low-Rank

Publication Year: 2018, Page(s):6488 - 6502
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Trajectory estimation of moving targets is examined; in particular, quasi-linear trajectories are considered. Background subtraction methods, exploiting low-rank backgrounds, and sparse features of interest are extended to incorporate linear constraints. The line constraint is enforced via a rotation that yields an additional low rank condition. The proposed method is applied to single object trac... View full abstract»

• ### Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach

Publication Year: 2018, Page(s):6503 - 6517
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Hyperspectral super-resolution refers to the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI) that admits fine spatial and spectral resolutions. State-of-the-art methods approach the problem via low-rank matrix approximations to the matricized HSI and MSI. These methods are effective to some extent, but a number of challenges r... 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
EEE Department
Imperial College London