# IEEE Transactions on Signal Processing

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### Tensor Decomposition for Signal Processing and Machine Learning

Publication Year: 2017, Page(s):3551 - 3582
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Tensors or multiway arrays are functions of three or more indices (i, j, k, . . . )-similar to matrices (two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a rich history, stretching over almost a century, and touching upon numerous disciplines; but they have only recently become ubiquitous in signal and data analytics at the confluence of signal processing,... View full abstract»

• ### $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

Publication Year: 2006, Page(s):4311 - 4322
Cited by:  Papers (2840)  |  Patents (27)
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In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity i... View full abstract»

• ### A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (4687)  |  Patents (108)
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Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, w... View full abstract»

• ### Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (132)
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During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like ... View full abstract»

• ### Fast Singular Value Shrinkage With Chebyshev Polynomial Approximation Based on Signal Sparsity

Publication Year: 2017, Page(s):6083 - 6096
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We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA). Many signal processing problems require iterative application of singular value decomposition (SVD) for minimizing the rank of a given data matrix with other cost functions and/or constraints, which is called matrix rank minimization. In matrix rank minimization, singular values o... View full abstract»

• ### Improved Pseudolinear Kalman Filter Algorithms for Bearings-Only Target Tracking

Publication Year: 2017, Page(s):6119 - 6134
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Despite its stability and computational complexity advantages, the pseudolinear Kalman filter (PLKF) suffers from severe bias problems in bearings-only target tracking applications. This paper develops new variants of the PLKF with significant performance improvement. First, a detailed analysis of the PLKF bias is provided for nearly constant-velocity target dynamics. This analysis uncovers the sp... View full abstract»

• ### Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1618)  |  Patents (75)
| | PDF (328 KB) | HTML

The use of space-division multiple access (SDMA) in the downlink of a multiuser multiple-input, multiple-output (MIMO) wireless communications network can provide a substantial gain in system throughput. The challenge in such multiuser systems is designing transmit vectors while considering the co-channel interference of other users. Typical optimization problems of interest include the capacity p... View full abstract»

• ### A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

Publication Year: 2006, Page(s):3852 - 3860
Cited by:  Papers (422)  |  Patents (21)
| | PDF (675 KB) | HTML

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore... View full abstract»

• ### On the Shift Operator, Graph Frequency, and Optimal Filtering in Graph Signal Processing

Publication Year: 2017, Page(s):6303 - 6318
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Defining a sound shift operator for graph signals, similar to the shift operator in classical signal processing, is a crucial problem in graph signal processing (GSP), since almost all operations, such as filtering, transformation, prediction, are directly related to the graph shift operator. We define a set of energy-preserving shift operators that satisfy many properties similar to their counter... View full abstract»

• ### Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications

Publication Year: 2017, Page(s):6201 - 6216
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In this paper, we propose a hybrid analog-digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain's s... View full abstract»

• ### Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (92)  |  Patents (1)
| | PDF (3301 KB) | HTML

Some recent methods, like the empirical mode decomposition (EMD), propose to decompose a signal accordingly to its contained information. Even though its adaptability seems useful for many applications, the main issue with this approach is its lack of theory. This paper presents a new approach to build adaptive wavelets. The main idea is to extract the different modes of a signal by designing an a... View full abstract»

• ### Nonlinear Kalman Filtering With Divergence Minimization

Publication Year: 2017, Page(s):6319 - 6331
| | PDF (833 KB) | HTML

We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and α-divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper, we propose novel algorithms to approximate this posterior by optimizing th... View full abstract»

Publication Year: 2017, Page(s):6292 - 6302
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Although a significant attention has been drawn to the concept of multiple-input multiple-output (MIMO) radar and in particular on the transmit beamforming design and multitarget localization, the cooperation between the transmitter (Tx) and the receiver (Rx) has been rarely investigated. In this paper, a novel joint Tx and Rx optimization approach for localization is proposed for MIMO radar, wher... View full abstract»

• ### Robust Large Margin Deep Neural Networks

Publication Year: 2017, Page(s):4265 - 4280
| | PDF (1289 KB) | HTML

The generalization error of deep neural networks via their classification margin is studied in this paper. Our approach is based on the Jacobian matrix of a deep neural network and can be applied to networks with arbitrary nonlinearities and pooling layers, and to networks with different architectures such as feed forward networks and residual networks. Our analysis leads to the conclusion that a ... View full abstract»

• ### Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (767)  |  Patents (3)
| | PDF (1561 KB) | HTML

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directl... View full abstract»

• ### A sparse signal reconstruction perspective for source localization with sensor arrays

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (739)  |  Patents (3)
| | PDF (704 KB) | HTML

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the ℓ1-norm. A number of recent theoretical results on sparsifying properties of ℓ1 penalties justify this choice. Explicitly enforcing the sparsit... View full abstract»

• ### Structured Compressed Sensing: From Theory to Applications

Publication Year: 2011, Page(s):4053 - 4085
Cited by:  Papers (341)  |  Patents (9)
| | PDF (1987 KB) | HTML

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, ... View full abstract»

• ### Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (1)
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This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also cov... View full abstract»

• ### Spectral Analysis of Stationary Random Bivariate Signals

Publication Year: 2017, Page(s):6135 - 6145
| | PDF (523 KB) | HTML

A novel approach toward the spectral analysis of stationary random bivariate signals is proposed. Unlike existing approaches, the proposed framework exhibits a natural link between well-defined statistical objects and physical parameters for bivariate signals. Using the quaternion Fourier transform, we introduce a quaternion-valued spectral representation of random bivariate signals seen as comple... View full abstract»

• ### Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

Publication Year: 2017, Page(s):5756 - 5769
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Polar codes have gained significant amount of attention during the past few years and have been selected as a coding scheme for the next generation of mobile broadband standard. Among decoding schemes, successive-cancellation list (SCL) decoding provides a reasonable tradeoff between the error-correction performance and hardware implementation complexity when used to decode polar codes, at the cos... View full abstract»

• ### Discrete Fractional Fourier Transforms Based on Closed-Form Hermite–Gaussian-Like DFT Eigenvectors

Publication Year: 2017, Page(s):6171 - 6184
| | PDF (1933 KB) | HTML

In this paper, we construct discrete fractional Fourier transforms (DFrFT) using recently introduced closed-form Hermite-Gaussian-like (HGL) eigenvectors. With respect to such eigenvectors, we discuss the convergence of their components to samples of the corresponding continuous Hermite-Gaussian functions and propose solutions to deal with some restrictions related to their construction. This allo... View full abstract»

• ### Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (137)
| | PDF (2577 KB) | HTML

In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph. The resulting signals (data indexed by the nodes) are far removed from time or image signals indexed by well ordered ... View full abstract»

• ### An Online Convex Optimization Approach to Proactive Network Resource Allocation

Publication Year: 2017, Page(s):6350 - 6364
| | PDF (958 KB) | HTML

Existing approaches to online convex optimization make sequential one-slot-ahead decisions, which lead to (possibly adversarial) losses that drive subsequent decision iterates. Their performance is evaluated by the so-called regret that measures the difference of losses between the online solution and the best yet fixed overall solution in hindsight. The present paper deals with online convex opti... View full abstract»

• ### LLR-Based Successive Cancellation List Decoding of Polar Codes

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (25)
| | PDF (4241 KB) | HTML

We show that successive cancellation list decoding can be formulated exclusively using log-likelihood ratios. In addition to numerical stability, the log-likelihood ratio based formulation has useful properties that simplify the sorting step involved in successive cancellation list decoding. We propose a hardware architecture of the successive cancellation list decoder in the log-likelihood ratio ... View full abstract»

• ### Innovation Pursuit: A New Approach to Subspace Clustering

Publication Year: 2017, Page(s):6276 - 6291
| | PDF (2370 KB) | HTML

In subspace clustering, a group of data points belonging to a union of subspaces are assigned membership to their respective subspaces. This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties. We present two frameworks in which the idea of innovation... View full abstract»

• ### Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network

Publication Year: 2017, Page(s):6397 - 6408
| | PDF (666 KB) | HTML

We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clus... View full abstract»

• ### A Fixed-Point Online Kernel Principal Component Extraction Algorithm

Publication Year: 2017, Page(s):6244 - 6259
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Kernel principal component analysis (KPCA) is a powerful and widely applied nonlinear feature extraction technique. However, as originally proposed, KPCA may be cumbersome or infeasible in large-scale datasets, which motivated the development of low-complexity iterative extraction algorithms, mainly aiming image processing applications. Recently, some online KPCA extraction algorithms were propose... View full abstract»

• ### AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

Publication Year: 2017, Page(s):4293 - 4308
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Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the ... View full abstract»

• ### An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Publication Year: 2011, Page(s):4331 - 4340
Cited by:  Papers (342)  |  Patents (1)
| | PDF (828 KB) | HTML

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorith... View full abstract»

• ### Nonuniform fast Fourier transforms using min-max interpolation

Publication Year: 2003, Page(s):560 - 574
Cited by:  Papers (419)  |  Patents (4)
| | PDF (879 KB) | HTML

The fast Fourier transform (FFT) is used widely in signal processing for efficient computation of the FT of finite-length signals over a set of uniformly spaced frequency locations. However, in many applications, one requires nonuniform sampling in the frequency domain, i.e., a nonuniform FT. Several papers have described fast approximations for the nonuniform FT based on interpolating an oversamp... View full abstract»

• ### Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis

Publication Year: 2017, Page(s):6260 - 6275
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This paper presents a remarkably simple, yet powerful, algorithm termed coherence pursuit (CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points. By contrast, outliers either do not admit low-dimensional structures or form small clusters. In ei... View full abstract»

• ### Nonlinear Chirp Mode Decomposition: A Variational Method

Publication Year: 2017, Page(s):6024 - 6037
| | PDF (2436 KB) | HTML

Variational mode decomposition (VMD), a recently introduced method for adaptive data analysis, has aroused much attention in various fields. However, the VMD is formulated based on the assumption of narrow-band property of the signal model. To analyze wide-band nonlinear chirp signals (NCSs), we present an alternative method called variational nonlinear chirp mode decomposition (VNCMD). The VNCMD ... View full abstract»

• ### Sparse Regularization via Convex Analysis

Publication Year: 2017, Page(s):4481 - 4494
| | PDF (961 KB) | HTML Media Code

Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alternative to the L1 norm, this paper proposes a class of nonconvex penalty functions that maintain the convexity of the least squares cost function to be minimized, and avoids the systematic underestimation characteristic o... View full abstract»

• ### Dynamical Sparse Recovery With Finite-Time Convergence

Publication Year: 2017, Page(s):6146 - 6157
| | PDF (1516 KB) | HTML

Eventhough sparse recovery (SR) has been successfully applied in a wide range of research communities, there still exists a barrier to real applications because of the inefficiency of the state-of-the-art algorithms. In this paper, we propose a dynamical approach to SR, which is highly efficient and with finite-time convergence property. First, instead of solving the 11 regularized optimization pr... View full abstract»

• ### Joint Beamforming and Power-Splitting Control in Downlink Cooperative SWIPT NOMA Systems

Publication Year: 2017, Page(s):4874 - 4886
| | PDF (1140 KB) | HTML

This paper investigates the application of simultaneous wireless information and power transfer (SWIPT) to cooperative non-orthogonal multiple access (NOMA). A new cooperative multiple-input single-output (MISO) SWIPT NOMA protocol is proposed, where a user with a strong channel condition acts as an energy-harvesting (EH) relay by adopting power splitting (PS) scheme to help a user with a poor cha... View full abstract»

• ### Stationary Graph Processes and Spectral Estimation

Publication Year: 2017, Page(s):5911 - 5926
| | PDF (891 KB) | HTML Media

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios, the information of interest resides in more irregular graph domains. This lack of regularity hampers the generalization of the classical notion of stationarity to graph signals. This paper propos... View full abstract»

• ### Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems

Publication Year: 2017, Page(s):3818 - 3832
| | PDF (1367 KB) | HTML

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subs... View full abstract»

• ### Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation

Publication Year: 2010, Page(s):4131 - 4143
Cited by:  Papers (97)  |  Patents (3)
| | PDF (1375 KB) | HTML

In multiple-input multiple-output (MIMO) systems, the use of many radio frequency (RF) and analog-to-digital converter (ADC) chains at the receiver is costly. Analog beamformers operating in the RF domain can reduce the number of antenna signals to a feasible number of baseband channels. Subsequently, digital beamforming is used to capture the desired user signal. In this paper, we consider the de... View full abstract»

• ### Transfer Learning in Adaptive Filters: The Nearest Instance Centroid-Estimation Kernel Least-Mean-Square Algorithm

Publication Year: 2017, Page(s):6520 - 6535
| | PDF (2296 KB) | HTML

We propose a novel nearest-neighbors approach to organize and curb the growth of radial basis function network in kernel adaptive filtering (KAF). The nearest-instance-centroid-estimation (NICE) kernel least-mean-square (KLMS) algorithm provides an appropriate time-space tradeoff with good performance. Its centers in the input/feature space are organized by quasi-orthogonal regions for greatly sim... View full abstract»

• ### An Optimization Perspective of the Superiority of NOMA Compared to Conventional OMA

Publication Year: 2017, Page(s):5191 - 5202
| | PDF (635 KB) | HTML

Existing work regarding the performance comparison between nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA) can be generally divided into two categories. The work in the first category aims to develop analytical results for the comparison, often with fixed system parameters. The work in the second category aims to propose efficient algorithms for optimizing these parameter... View full abstract»

• ### Comments on “Enhanced PUMA for Direction-of-Arrival Estimation and Its Performance Analysis”

Publication Year: 2017, Page(s):6113 - 6114
| | PDF (78 KB) | HTML

We show that the recently proposed (enhanced) principal-singular-vector utilization for modal analysis (PUMA) estimator for array processing [C. Qian, L. Huang, N. Sidiropoulos, and H. C. So, “Enhanced PUMA for direction-of-arrival estimation and its performance analysis,” IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4127-4137, Aug. 2016], minimizes the same criterion function a... View full abstract»

• ### A Sampling Framework for Solving Physics-Driven Inverse Source Problems

Publication Year: 2017, Page(s):6365 - 6380
| | PDF (1171 KB) | HTML

Partial differential equations are central to describing many physical phenomena. In many applications, these phenomena are observed through a sensor network, with the aim of inferring their underlying properties. Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear par... View full abstract»

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

Publication Year: 2017, Page(s):5289 - 5304
| | PDF (1529 KB) | HTML

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»

• ### Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (3821)  |  Patents (120)
| | PDF (2488 KB)

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit d... View full abstract»

• ### Sampling signals with finite rate of innovation

Publication Year: 2002, Page(s):1417 - 1428
Cited by:  Papers (455)  |  Patents (34)
| | PDF (488 KB) | HTML

The authors consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials. Even though these signals are not bandlimited, we show that they can be sampled uniformly at (o... View full abstract»

• ### Improving Wireless Physical Layer Security via Cooperating Relays

Publication Year: 2010, Page(s):1875 - 1888
Cited by:  Papers (588)
| | PDF (943 KB) | HTML

Physical (PHY) layer security approaches for wireless communications can prevent eavesdropping without upper layer data encryption. However, they are hampered by wireless channel conditions: absent feedback, they are typically feasible only when the source-destination channel is better than the source-eavesdropper channel. Node cooperation is a means to overcome this challenge and improve the perf... View full abstract»

• ### Correntropy: Properties and Applications in Non-Gaussian Signal Processing

Publication Year: 2007, Page(s):5286 - 5298
Cited by:  Papers (269)
| | PDF (682 KB) | HTML

The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are present... View full abstract»

• ### The kernel recursive least-squares algorithm

Publication Year: 2004, Page(s):2275 - 2285
Cited by:  Papers (359)  |  Patents (2)
| | PDF (432 KB) | HTML

We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a high-dimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum mean-squared-error solutions to nonlinear least-squares problems that are frequently encountered in signal processing applications. In order to regularize sol... View full abstract»

• ### Sparse solutions to linear inverse problems with multiple measurement vectors

Publication Year: 2005, Page(s):2477 - 2488
Cited by:  Papers (533)  |  Patents (9)
| | PDF (528 KB) | HTML

We address the problem of finding sparse solutions to an underdetermined system of equations when there are multiple measurement vectors having the same, but unknown, sparsity structure. The single measurement sparse solution problem has been extensively studied in the past. Although known to be NP-hard, many single-measurement suboptimal algorithms have been formulated that have found utility in ... View full abstract»

• ### Analysis and Design of Optimum Sparse Array Configurations for Adaptive Beamforming

Publication Year: 2018, Page(s):340 - 351
| | PDF (1171 KB) | HTML

In this paper, we analyze the effect of nonuniform array configurations on adaptive beamforming for enhanced signal-to-interference-plus-noise ratio (SINR). The array is configured using a given number of antennas or through a selection of subset of antennas from a larger available set, leading to a sparse array in both cases. The bounds on the highest achievable SINR for a given number of antenna... 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