# IEEE Transactions on Signal Processing

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

Publication Year: 2017, Page(s):3551 - 3582
Cited by:  Papers (4)
| | PDF (1166 KB) | HTML Media

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»

• ### Variational Mode Decomposition

Publication Year: 2014, Page(s):531 - 544
Cited by:  Papers (222)
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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»

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

Publication Year: 2002, Page(s):174 - 188
Cited by:  Papers (5062)  |  Patents (113)
| | PDF (355 KB) | HTML

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»

• ### Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

Publication Year: 2015, Page(s):6169 - 6183
Cited by:  Papers (51)
| | PDF (3184 KB) | HTML

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot des... 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 (3251)  |  Patents (35)
| | PDF (1725 KB) | HTML

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»

• ### Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM

Publication Year: 2018, Page(s):3933 - 3945
| | PDF (841 KB) | HTML

In this paper, tradeoff and convergence issues for incentive mechanisms are addressed by combining optimization and game theory. Specifically, a multiple-leader multiple-follower (MLMF) game-based alternating direction method of multipliers (ADMM) is developed that incentivizes the agents to perform a group of controllers' tasks in order to satisfy their corresponding objectives. Both analytical a... View full abstract»

• ### Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems

Publication Year: 2018, Page(s):3393 - 3406
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When there are a large number of antennas in massive MIMO systems, the transmitted wideband signal will be sensitive to the physical propagation delay of electromagnetic waves across the large array aperture, which is called the spatial-wideband effect. In this scenario, the transceiver design is different from most of the existing works, which presume that the bandwidth of the transmitted signals... View full abstract»

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

Publication Year: 2004, Page(s):461 - 471
Cited by:  Papers (1711)  |  Patents (83)
| | 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 (464)  |  Patents (27)
| | 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»

• ### Empirical Wavelet Transform

Publication Year: 2013, Page(s):3999 - 4010
Cited by:  Papers (140)  |  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»

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

Publication Year: 2005, Page(s):3010 - 3022
Cited by:  Papers (847)  |  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»

• ### Particle Smoothing for Conditionally Linear Gaussian Models as Message Passing Over Factor Graphs

Publication Year: 2018, Page(s):3633 - 3648
| | PDF (1014 KB) | HTML

In this paper, the fixed-lag smoothing problem for conditionally linear Gaussian state-space models is investigated from a factor graph perspective. More specifically, after formulating Bayesian smoothing for an arbitrary state-space model as a forward-backward message passing over a factor graph, we focus on the above-mentioned class of models and derive two novel particle smoothers for it. Both ... View full abstract»

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

Publication Year: 2015, Page(s):5165 - 5179
Cited by:  Papers (41)  |  Patents (1)
| | 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»

• ### Optimal Training Design for MIMO Systems With General Power Constraints

Publication Year: 2018, Page(s):3649 - 3664
| | PDF (1197 KB) | HTML

Training design for general multiple-input multiple-output (MIMO) systems is investigated in this paper. Unlike prior designs that are applicable only for centralized MIMO systems with total power constraints, general power constraints are considered here. They cover total power constraints, individual power constraints, and mixed individual and per-user sum-power constraints as special cases. By ... View full abstract»

• ### Robust Chance Constrained Power Allocation Scheme for Multiple Target Localization in Colocated MIMO Radar System

Publication Year: 2018, Page(s):3946 - 3957
| | PDF (1322 KB) | HTML

Taking into account the probabilistic uncertainty on the target radar cross section (RCS) parameter, a robust chance constrained power allocation (RCC-PA) scheme is presented for multiple target localization in colocated multiple-input multiple-output radar system. Such a system adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized to illuminate multiple ta... View full abstract»

• ### Bayesian Compressive Sensing

Publication Year: 2008, Page(s):2346 - 2356
Cited by:  Papers (849)  |  Patents (5)
| | 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»

• ### Spectral Domain Sampling of Graph Signals

Publication Year: 2018, Page(s):3752 - 3767
| | PDF (2545 KB) | HTML

Sampling methods for graph signals in the graph spectral domain are presented. Though the conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired characteristics in regard to the graph spectral domain. With the proposed methods, the down- and upsampled graph signals inherit the frequency-domain characteristics of the sampled signa... View full abstract»

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

Publication Year: 2003, Page(s):560 - 574
Cited by:  Papers (454)  |  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»

• ### MIMO Radar and Cellular Coexistence: A Power-Efficient Approach Enabled by Interference Exploitation

Publication Year: 2018, Page(s):3681 - 3695
| | PDF (1091 KB) | HTML

We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power efficient transmission. This paper focuses on two optimization proble... View full abstract»

• ### Optimal Bayesian Transfer Learning

Publication Year: 2018, Page(s):3724 - 3739
| | PDF (1047 KB) | HTML

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework, in the homogeneous transfer learning scenario, where the so... View full abstract»

• ### A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices

Publication Year: 2018, Page(s):4079 - 4089
| | PDF (613 KB) | HTML

In this paper, we present a new algorithm for compressive sensing that makes use of binary measurement matrices and achieves exact recovery of ultrasparse vectors in a single pass and without any iterations. Due to its noniterative nature, our algorithm is hundreds of times faster than ℓ1-norm minimization and methods based on expander graphs, both of which require multiple itera... View full abstract»

• ### Spatial diversity in radars-models and detection performance

Publication Year: 2006, Page(s):823 - 838
Cited by:  Papers (734)  |  Patents (2)
| | PDF (608 KB) | HTML

Inspired by recent advances in multiple-input multiple-output (MIMO) communications, this proposal introduces the statistical MIMO radar concept. To the authors' knowledge, this is the first time that the statistical MIMO is being proposed for radar. The fundamental difference between statistical MIMO and other radar array systems is that the latter seek to maximize the coherent processing gain, w... View full abstract»

• ### Beamforming Optimization for Physical Layer Security in MISO Wireless Networks

Publication Year: 2018, Page(s):3710 - 3723
| | PDF (947 KB) | HTML

A wireless network of multiple transmitter-user pairs overheard by an eavesdropper, where the transmitters are equipped with multiple antennas, while the users and eavesdropper are equipped with a single antenna, is considered. At different levels of wireless channel knowledge, the problem of interest is beamforming to optimize the users' quality-of-service (QoS) in terms of their secrecy throughp... View full abstract»

• ### Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

Publication Year: 2006, Page(s):884 - 893
Cited by:  Papers (394)  |  Patents (3)
| | PDF (384 KB) | HTML

In this paper, we study the performance of multiple-input multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the convent... View full abstract»

• ### Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies

Publication Year: 2018, Page(s):3584 - 3598
| | PDF (1288 KB) | HTML

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed m... View full abstract»

• ### L1-Norm Principal-Component Analysis of Complex Data

Publication Year: 2018, Page(s):3256 - 3267
| | PDF (981 KB) | HTML

L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has attracted significant research interest over the past decade. L1-PCA of complex-valued data remains to date unexplored despite the many possible applications (in communication systems, for example). In this paper, we establish theoretical and algorithmic foundations of L1-PCA of complex-valued data matrices. Specifically, we fir... View full abstract»

• ### The Gaussian Mixture Probability Hypothesis Density Filter

Publication Year: 2006, Page(s):4091 - 4104
Cited by:  Papers (625)  |  Patents (7)
| | PDF (1066 KB) | HTML

A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis densi... View full abstract»

• ### Learning the MMSE Channel Estimator

Publication Year: 2018, Page(s):2905 - 2917
| | PDF (1074 KB) | HTML

We present a method for estimating conditionally Gaussian random vectors with random covariance matrices, which uses techniques from the field of machine learning. Such models are typical in communication systems, where the covariance matrix of the channel vector depends on random parameters, e.g., angles of propagation paths. If the covariance matrices exhibit certain Toeplitz and shift-invarianc... View full abstract»

• ### Online Nonlinear Estimation via Iterative $L^2$ -Space Projections: Reproducing Kernel of Subspace

Publication Year: 2018, Page(s):4050 - 4064
| | PDF (2301 KB) | HTML

We propose a novel online learning paradigm for nonlinear-function estimation tasks based on the iterative projections in the L2 space with probability measure reflecting the stochastic property of input signals. The proposed learning algorithm exploits the reproducing kernel of the so-called dictionary subspace, based on the fact that any finite-dimensional space of functions has a rep... View full abstract»

• ### Matching pursuits with time-frequency dictionaries

Publication Year: 1993, Page(s):3397 - 3415
Cited by:  Papers (4109)  |  Patents (128)
| | 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»

• ### Large-Scale Robust Beamforming via $ell _{infty }$ -Minimization

Publication Year: 2018, Page(s):3824 - 3837
| | PDF (1605 KB) | HTML

In this paper, linearly constrained and robust ℓ∞-norm beamforming techniques are proposed for non-Gaussian signals. A conventional approach for ℓ∞-minimization needs to solve a linear programming (LP) or second-order cone programming (SOCP). However, this strategy is computationally prohibitive for “big data” because the existing alg... View full abstract»

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

Publication Year: 2017, Page(s):794 - 816
Cited by:  Papers (6)
| | PDF (1188 KB) | HTML

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»

• ### Alias-Free Products of Signals Near Nyquist Rate

Publication Year: 2018, Page(s):4151 - 4159
| | PDF (1352 KB) | HTML

Products of time-series signals have found wide-spread application in many fields of signal processing. For example, they are often used in modeling and compensating distortions generated by analog and mixed-signal components. Without excess bandwidth, the products of time-series signals will produce aliased artifacts that do not represent the physical phenomenology of the components being modeled... View full abstract»

• ### Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

Publication Year: 2018, Page(s):4090 - 4104
| | PDF (1223 KB) | HTML Media

The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional neural networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to a pursuit algorithm aiming to estimate the nested sparse representation vectors from a given input signal. Despite having served as... View full abstract»

• ### Hybrid Beamforming With Selection for Multiuser Massive MIMO Systems

Publication Year: 2018, Page(s):4105 - 4120
| | PDF (1232 KB) | HTML

This paper studies a variant of hybrid beamforming, namely, hybrid beamforming with selection (HBwS), as an attractive solution to reduce the hardware cost of multiuser Massive Multiple-Input-Multiple-Output systems, while retaining good performance. Unlike conventional hybrid beamforming, in a transceiver with HBwS, the antenna array is fed by an analog beamforming matrix with L̅ input por... View full abstract»

• ### Discrete Signal Processing on Graphs

Publication Year: 2013, Page(s):1644 - 1656
Cited by:  Papers (223)
| | 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»

• ### Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

Publication Year: 2017, Page(s):4075 - 4089
Cited by:  Papers (4)
| | PDF (1223 KB) | HTML

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output system. Each receiver antenna of the base station is assumed to be equipped with a pair of one-bit analog-to-digital converters to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for... View full abstract»

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

Publication Year: 2010, Page(s):1875 - 1888
Cited by:  Papers (675)
| | 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 (331)
| | 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»

• ### Sparse Representation Using Multidimensional Mixed-Norm Penalty With Application to Sound Field Decomposition

Publication Year: 2018, Page(s):3327 - 3338
| | PDF (1197 KB) | HTML

A sparse representation method for multidimensional signals is proposed. In generally used group-sparse representation algorithms, the sparsity is imposed only on a single dimension and the signals in the other dimensions are solved in the least-square-error sense. However, multidimensional signals can be sparse in multiple dimensions. For example, in acoustic array processing, in addition to the ... View full abstract»

• ### Deconstructing multiantenna fading channels

Publication Year: 2002, Page(s):2563 - 2579
Cited by:  Papers (391)  |  Patents (5)
| | PDF (636 KB) | HTML

Accurate and tractable channel modeling is critical to realizing the full potential of antenna arrays in wireless communications. Current approaches represent two extremes: idealized statistical models representing a rich scattering environment and parameterized physical models that describe realistic scattering environments via the angles and gains associated with different propagation paths. How... View full abstract»

• ### Grid Evolution Method for DOA Estimation

Publication Year: 2018, Page(s):2374 - 2383
| | PDF (767 KB) | HTML

Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Furthermore, these methods fail if more than one true DOA is located in a same grid interval. In order to s... View full abstract»

• ### Global Optimality in Low-Rank Matrix Optimization

Publication Year: 2018, Page(s):3614 - 3628
| | PDF (846 KB) | HTML

This paper considers the minimization of a general objective function f(X) over the set of rectangular n × m matrices that have rank at most r. To reduce the computational burden, we factorize the variable X into a product of two smaller matrices and optimize over these two matrices instead of X. Despite the resulting nonconvexity, recent studies in matrix completion and sensing have shown ... View full abstract»

• ### Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference

Publication Year: 2013, Page(s):38 - 43
Cited by:  Papers (119)
| | PDF (1463 KB) | HTML

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficulties in practical situations where true DOAs are not on the discretized samplin... View full abstract»

• ### Spatial Lattice Modulation for MIMO Systems

Publication Year: 2018, Page(s):3185 - 3198
| | PDF (1229 KB) | HTML

This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multiple-input multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multidimensional signal set that consists of lattice points. One major finding is that SLM achieves a higher spectral efficiency than the existi... View full abstract»

• ### Generalized Coprime Array Configurations for Direction-of-Arrival Estimation

Publication Year: 2015, Page(s):1377 - 1390
Cited by:  Papers (96)
| | PDF (3409 KB) | HTML

A coprime array uses two uniform linear subarrays to construct an effective difference coarray with certain desirable characteristics, such as a high number of degrees-of-freedom for direction-of-arrival (DOA) estimation. In this paper, we generalize the coprime array concept with two operations. The first operation is through the compression of the inter-element spacing of one subarray and the re... View full abstract»

• ### Capacity Analysis of One-Bit Quantized MIMO Systems With Transmitter Channel State Information

Publication Year: 2015, Page(s):5498 - 5512
Cited by:  Papers (61)
| | PDF (3323 KB) | HTML Media

With bandwidths on the order of a gigahertz in emerging wireless systems, high-resolution analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution is to employ low resolution one-bit ADCs. In this paper, we analyze the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADCs. Channel state information is assumed to be known at both the transmitter ... View full abstract»

• ### Particle filters for positioning, navigation, and tracking

Publication Year: 2002, Page(s):425 - 437
Cited by:  Papers (752)  |  Patents (20)
| | PDF (335 KB) | HTML

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position de... View full abstract»

• ### DOA Estimation Using Compressed Sparse Array

Publication Year: 2018, Page(s):4133 - 4146
| | PDF (1159 KB) | HTML

Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of frontend circuit chains. In this paper, we generalized current works on the two categories of methods to a ... View full abstract»

• ### Sparsity Order Estimation From a Single Compressed Observation Vector

Publication Year: 2018, Page(s):3958 - 3971
| | PDF (732 KB) | HTML

In this paper, the problem of estimating the unknown degree of sparsity from compressive measurements without the need to carry out a sparse recovery step is investigated. While the sparsity order can be directly inferred from the effective rank of the observation matrix in the multiple snapshot case, this appears to be impossible in the more challenging single snapshot case. It is shown that spec... 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