# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 20 of 20

Publication Year: 2019, Page(s):1964 - 1977
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Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collected at spatially distributed sensors, radio tomography capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at each location along the propagation path. These SLFs can be utilized for... View full abstract»

• ### Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization

Publication Year: 2019, Page(s):1978 - 1991
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This paper addresses the design and analysis of feedback-based online algorithms to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. The emerging time-varying convex optimization formalism is leveraged to model optimal operational trajectories of the systems, as well as explicit local and network-level operational constrain... View full abstract»

• ### Measurement Bounds for Observability of Linear Dynamical Systems Under Sparsity Constraints

Publication Year: 2019, Page(s):1992 - 2006
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In this paper, we address the problem of observability of a linear dynamical system from compressive measurements and the knowledge of its external inputs. Observability of a high-dimensional system state in general requires a correspondingly large number of measurements. We show that if the initial state vector admits a sparse representation, the number of measurements can be significantly reduce... View full abstract»

• ### Exploiting Dynamic Sparsity for Downlink FDD-Massive MIMO Channel Tracking

Publication Year: 2019, Page(s):2007 - 2021
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Accurate channel tracking with a small pilot overhead is vital for real-time massive multiple-input and multiple-output (MIMO) communication over a dynamic channel. Recently, compressive sensing has been applied to reduce the pilot overheads by exploiting the spatial and/or temporal correlation of massive MIMO channels. However, most existing channel estimation and tracking algorithms are based on... View full abstract»

• ### Algorithms and Fundamental Limits for Unlabeled Detection Using Types

Publication Year: 2019, Page(s):2022 - 2035
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We deal with the classical problem of testing two simple statistical hypotheses but, as a new element, it is assumed that the data vector is observed after an unknown permutation of its entries. What is the fundamental limit for the detection performance in this case? How much information for detection is contained in the entry values and how much in their positions? In the first part of this pape... View full abstract»

• ### Power Systems Topology and State Estimation by Graph Blind Source Separation

Publication Year: 2019, Page(s):2036 - 2051
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In this paper, we consider the problem of blind estimation of states and topology (BEST) in power systems. We use the linearized dc model of real power measurements with unknown voltage phases (i.e., states) and an unknown admittance matrix (i.e., topology) and show that the BEST problem can be formulated as a blind source separation (BSS) problem with a weighted Laplacian mixing matrix. We develo... View full abstract»

• ### Thinned Coprime Array for Second-Order Difference Co-Array Generation With Reduced Mutual Coupling

Publication Year: 2019, Page(s):2052 - 2065
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In this work, we present a new coprime array structure termed thinned coprime array (TCA), which exploits the redundancy in the structure of existing coprime array and achieves the same virtual aperture and degrees of freedom (DOFs) as the conventional coprime array with much fewer number of sensors. In comparison to other sparse arrays, thinned coprime arrays possess more unique lags (total numbe... View full abstract»

• ### Fusion of Correlated Decisions Using Regular Vine Copulas

Publication Year: 2019, Page(s):2066 - 2079
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In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities. It can express a multivariate copula by using a cascade of bivariate copulas, the so-called pair copulas. Assuming that local detectors are single threshold b... View full abstract»

• ### FADE: Fast and Asymptotically Efficient Distributed Estimator for Dynamic Networks

Publication Year: 2019, Page(s):2080 - 2092
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Consider a set of agents that wish to estimate a vector of parameters. For this estimation goal, each agent can measure (in additive Gaussian noise) linear combinations of the unknown vector of parameters and can broadcast information to a few other neighbors. To coordinate the agents, we propose a distributed algorithm called FADE (fast and asymptotically efficient distributed estimator). FADE en... View full abstract»

• ### K-Medoids Clustering of Data Sequences With Composite Distributions

Publication Year: 2019, Page(s):2093 - 2106
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This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from unknown continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intracluster distance is assumed to be small... View full abstract»

• ### A Robust Spectral Estimator With Application to a Noise-Corrupted Process

Publication Year: 2019, Page(s):2107 - 2114
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When a dataset is corrupted by noise, the model for data generating process is misspecified and can cause parameter-estimation problems. For example, in the case of a Gaussian autoregressive (AR) process corrupted by noise, data are more accurately modeled as an AR–moving-average process rather than an AR process. This misspecification leads to bias, and hence, low resolution in AR spectral estima... View full abstract»

• ### Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection

Publication Year: 2019, Page(s):2115 - 2128
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The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among $M$ sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In th... View full abstract»

• ### Adaptive Global Time Sequence Averaging Method Using Dynamic Time Warping

Publication Year: 2019, Page(s):2129 - 2142
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Time sequence averaging under dynamic time warping (DTW) is a non-trivial problem, and its exact solution requires unrealistic computational complexity in practice. The DTW barycenter averaging (DBA) method is one of the most effective iterative approximation solutions to date. However, there are still a few drawbacks in the DBA method. First, the length of the resulting average sequence depends o... View full abstract»

• ### Frequency Synchronization for Uplink Massive MIMO With Adaptive MUI Suppression in Angle Domain

Publication Year: 2019, Page(s):2143 - 2158
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In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output multiuser network, which is applicable for users with either separate or overlapped angle-of-arrival (AoA) regions. For each user, we first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-... View full abstract»

• ### Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM

Publication Year: 2019, Page(s):2159 - 2172
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The autoregressive (AR) model is a widely used model to understand time series data. Traditionally, the innovation noise of the AR is modeled as Gaussian. However, many time series applications, for example, financial time series data, are non-Gaussian, therefore, the AR model with more general heavy-tailed innovations is preferred. Another issue that frequently occurs in time series is missing va... View full abstract»

• ### Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

Publication Year: 2019, Page(s):2173 - 2186
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We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, the exhaustive search procedure used in each iteration of 1bRELAX is time-consuming. In... View full abstract»

• ### Covariance Matrix Estimation From Linearly-Correlated Gaussian Samples

Publication Year: 2019, Page(s):2187 - 2195
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Covariance matrix estimation concerns the problem of estimating the covariance matrix from a collection of samples, which is of extreme importance in many applications. Classical results have shown that $O(n)$ samples are sufficient to accurately estimate the covariance matrix from $n$... View full abstract»

• ### Localization From Incomplete Euclidean Distance Matrix: Performance Analysis for the SVD–MDS Approach

Publication Year: 2019, Page(s):2196 - 2209
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Localizing a cloud of points from noisy measurements of a subset of pairwise distances has applications in various areas, such as sensor network localization and reconstruction of protein conformations from nuclear magnetic resonance measurements. Drineas et al. proposed a natural two-stage approach, named singular value decomposition (SVD)–multidimensional scaling (MDS), for this... View full abstract»

• ### Detection of Sparse Stochastic Signals With Quantized Measurements in Sensor Networks

Publication Year: 2019, Page(s):2210 - 2220
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In this paper, we consider the problem of detection of sparse stochastic signals with quantized measurements in sensor networks. The observed sparse signals are assumed to follow the Bernoulli–Gaussian distribution. Due to the limited bandwidth in sensor networks, the local sensors are required to send quantized measurements to the fusion center. First, we propose a detector using the locally most... View full abstract»

• ### New Saddle-Point Technique for Non-Coherent Radar Detection With Application to Correlated Targets in Uncorrelated Clutter Speckle

Publication Year: 2019, Page(s):2221 - 2233
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This paper presents a novel implementation of the saddle-point technique for calculating the non-coherent detection probability of targets by airborne surveillance radar in the presence of pulse-to-pulse correlation. It is based on the inverse Laplace transform of the moment generating function for the returned power distribution by means of integration along the path of steepest descent. To illus... 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