# IEEE Transactions on Signal Processing This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

**Stability Analysis of $ell _{0,infty }$-Norm Based Convolutional Sparse Coding Using Stripe Coherence**

Theoretical guarantees for the $ell _{0,infty }$-pseudo-norm based convolutional sparse coding have been established in a recent work. However, the stability analysis in the noisy case via the stripe coherence is absent. This coherence is a stronger characterization of the convolutional dictionary, and a considerably more informative measure than the standard global mutual coherence. The present paper supplements this missing part. Formally, three main results together with their proofs are given. The first one is for the stability of the solution to the $P_{0,infty }^{epsilon }$ problem, the second one and the third one a...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Variational Temporal Deep Generative Model for Radar HRRP Target Recognition**

We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP. The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. For scalable training and fast out-of-sample prediction, we propose the hybrid of a stochastic-gradient Markov chain Monte Carlo (MCMC) and a recurrent variational inference model to perform posterior inference. To utilize the label information to extract more discriminative latent representations, we fu...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Spatial GNSS Spoofing Against Drone Swarms With Multiple Antennas and Wiener Filter**

Spoofing of global-navigation satellite system (GNSS) signals to induce a target position estimate is a relevant security threat to the navigation of drones. However, spoofing multiple drones simultaneously as they move in a swarm, without disrupting their formation, is a complex task. In this paper, we propose to transmit spoofing signals from the ground, such that the fake position can be estimated in any point of an area of the plane where the swarm is moving. To this end we filter the satellite-generated GNSS signals with a multidimensional linear filter, and transmit the filtered signal with multiple ground antennas. ...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Privacy-Preserving Incremental ADMM for Decentralized Consensus Optimization**

The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communication efficiency and privacy-preservation of ADMM by solving the consensus optimization problem over decentralized networks. Since walk algorithms can reduce communication load, we first propose incremental ADMM (I-ADMM) based on the walk algorithm, the updating order of ...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Localization of a Moving Object With Sensors in Motion by Time Delays and Doppler Shifts**

This paper investigates the problem of active localization of a moving object in its initial position and velocity, using time delay only or with Doppler shift measurements acquired by a number of monostatic sensors. Each sensor has non-negligible motion during the observation period, causing it at different positions when it sends and receives the signal, with the separation proportional to the signal travel time in reaching the object and returning back. The object is not at the same position when it reflects the signals from various sensors due to its motion. Both motion effects lead to recursive model equations for tim...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Brain Decoding of Viewed Image Categories via Semi-Supervised Multi-View Bayesian Generative Model**

Brain decoding has shown that viewed image categories can be estimated from evoked functional magnetic resonance imaging (fMRI) activity. Recent studies attempted to estimate viewed image categories that were not used for training previously. Nevertheless, the estimation performance is limited since it is difficult to collect a large amount of fMRI data for training. This paper presents a method to accurately estimate viewed image categories not used for training via a semi-supervised multi-view Bayesian generative model. Our model focuses on the relationship between fMRI activity and multiple modalities, i.e., visual feat...

**Source: **IEEE Transactions on Signal Processing - October 20, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Analysis of the SNR Loss Distribution With Covariance Mismatched Training Samples**

We analyze the distribution of the signal to noise ratio (SNR) loss at the output of an adaptive filter which is trained with samples that do not share the same covariance matrix as the samples for which the filter is foreseen. Our objective is to find an accurate approximation of the distribution of the SNR loss which has a similar form as in the case of no mismatch. We successively consider the case where the two covariance matrices satisfy the so-called generalized eigenrelation, and the case where they are arbitrary. In the former case, this amounts to approximate a central quadratic form in normal variables while the ...

**Source: **IEEE Transactions on Signal Processing - October 16, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Generalized Fixed-Point Continuation Method: Convergence and Application**

In this paper, we consider a class of minimization problems with the objective functions having a form of summation of a penalized differentiable convex function, and a weighted $ell _1$-norm. However, different from the common assumption of positive weights in existing studies, we shall address a general case where the weights can be either positive or negative, motivated by the fact that negative weights are also capable of inducing sparsity, and even achieving outstanding performance. To deal with the resulting problem, a generalized fixed-point continuation (GFPC) method is introduced, and an accelerated variant is dev...

**Source: **IEEE Transactions on Signal Processing - October 16, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Tractable Inference and Observation Likelihood Evaluation in Latent Structure Influence Models**

Latent Structure Influence Models (LSIMs) are a particular kind of Coupled Hidden Markov Models (CHMMs). Against CHMMs, LSIMs overcome the exponential growth of state-space parameters by considering the influence model for coupled Markov chains. Nevertheless, the exact inference in LSIMs requires exponential complexity. We propose a new recursive formulation to compute marginal forward and backward parameters by $mathcal {O}(T{(NC)}^{2})$ instead of $mathcal {O}(TN^{text{2},C})$ for $C$ channels of $N$ states apiece observing $T$ data points. This formulation is derived systematically and carefully to increase the inferenc...

**Source: **IEEE Transactions on Signal Processing - October 16, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Maximum Likelihood Detection in the Presence of Non-Gaussian Jamming**

We consider a scenario in which a transmitter sends complex symbols drawn from multi-dimensional constellations to a receiver in the presence of a jammer emitting proactively and continuously a zero-mean complex Gaussian signal over an unknown complex Gaussian channel. The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver. For this scenario, we develop the optimal maximum likelihood (ML) detector for cases corresponding to whether the receiver has full channel state information (CSI), full channel distribution information (CDI), or par...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Multi-Carrier Agile Phased Array Radar**

Modern radar systems are expected to operate reliably in congested environments. A candidate technology for meeting these demands is frequency agile radar (FAR), which randomly changes its carrier frequencies. FAR is known to improve the electronic counter-countermeasures (ECCM) performance while facilitating operation in congested setups. To enhance the target recovery performance of FAR in complex electromagnetic environments, we propose two radar schemes extending FAR to multi-carrier waveforms. The first is Wideband Multi-carrier Agile Radar (WMAR), which transmits/receives wideband waveforms simultaneously with every ...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Multilabel Classification With Multivariate Time Series Predictors**

In many applications, multilabel classification involves time-series predictors, as in multilabel video classification. How to account for the temporal dependencies with respect to input variables remains an issue, especially in action learning from videos. Motivated by the problem of video categorization and captioning, we propose a nonlinear multilabel classifier based on a hidden Markov model and a weighted loss separating false positive and negative classification errors. This allows us to account for label dependence and temporal dependencies of input variables in classification. Computationally, we derive a decomposa...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Stability Properties of Graph Neural Networks**

Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in recommender systems, power outage prediction, and motion planning, among others. GNNs consist of a cascade of layers, each of which applies a graph convolution, followed by a pointwise nonlinearity. In this work, we study the impact that changes in the underlying topology have on the output of the GNN. First, we show that GNNs are permutation equivariant, which implies that they effectively exploit internal symmetries of the underlying topology. Then, we prove that graph convolutions with integral L...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Phase-Only Robust Minimum Dispersion Beamforming**

A phase-only robust minimum dispersion (PO-RMD) beamformer is devised for non-Gaussian signals. Unlike conventional beamformers that adjust the complex-valued weights, including both amplitude and phase, of each antenna to fulfill spatial filtering, the proposed PO-RMD employs a unit-modulus constraint on the weights, which is equivalent to simply phase shifting at each antenna. Instead of the widely used minimum variance criterion, the PO-RMD adopts the minimum dispersion criterion, which minimizes the $ell _p$-norm of the array output to utilize the non-Gaussianity of the signals. To achieve robustness against model unce...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Cooperative Detection by Multi-Agent Networks in the Presence of Position Uncertainty**

Target detection in multi-agent networks encompasses a wide range of applications in both commercial and military contexts. Different from their conventional counterparts with static agents, mobile networks provide a natural alternative for performing detection tasks due to their advantages of flexibility and coverage. In this paper, we first establish a general framework of joint target detection and self-localization in mobile multi-agent networks. Next, we propose a cooperative target detection scheme and derive a generalized likelihood ratio test detector via direct localization of target and agents. The lower and uppe...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters**

The paper introduces a novel family of deterministic periodic signals, the orthogonal periodic sequences (OPSs), that allow the perfect identification on a finite period of any functional link polynomials (FLiP) filter with the cross-correlation method. The class of FLiP filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. The novel sequences share many properties of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of FLiP filters with the cross-correlation method. But, while PPSs exist only for orthogonal FL...

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Efficient Least Residual Greedy Algorithms for Sparse Recovery**

We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms. (Source: IEEE Transactions on Signal Processing)

**Source: **IEEE Transactions on Signal Processing - October 13, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Quaternion-Based Bilinear Factor Matrix Norm Minimization for Color Image Inpainting**

As a new color image representation tool, quaternion has achieved excellent results in the color image processing, because it treats the color image as a whole rather than as a separate color space component, thus it can make full use of the high correlation among RGB channels. Recently, low-rank quaternion matrix completion (LRQMC) methods have proven very useful for color image inpainting. In this article, we propose three novel LRQMC methods based on three quaternion-based bilinear factor (QBF) matrix norm minimization models. Specifically, we define quaternion double Frobenius norm (Q-DFN), quaternion double nuclear no...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Riemannian Geometric Optimization Methods for Joint Design of Transmit Sequence and Receive Filter on MIMO Radar**

In this paper, we study the joint design of a transmit sequence and a receive filter for an airborne multiple-input multiple-output (MIMO) radar system to improve its moving target detection performance in the presence of signal-dependent interference. The optimization problem is formulated to maximize the output signal-to-noise-plus-interference ratio (SINR), subject to the waveform constant-envelope (CE) constraint. To address the challenge of this non-convex problem, we propose a novel optimization framework for solving the problem over a Riemannian manifold which is the product of complex circles and a Euclidean space....

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Channel Estimation: Unified View of Optimal Performance and Pilot Sequences**

Channel estimation is of paramount importance in most communication systems in order to optimize the data rate/energy consumption tradeoff. In modern systems, the possibly large number of transmit/receive antennas and subcarriers makes this task difficult. Designing pilot sequences of reasonable size yielding good performance is thus critical. Classically, the number of pilots is reduced by viewing the channel as a random vector and assuming knowledge of its distribution. In practice, this requires estimating the channel covariance matrix, which can be computationally costly and not adapted to scenarios with high mobility....

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**System Identification of High-Dimensional Linear Dynamical Systems With Serially Correlated Output Noise Components**

We consider identification of linear dynamical systems comprising of high-dimensional signals, where the output noise components exhibit strong serial, and cross-sectional correlations. Although such settings occur in many modern applications, such dependency structure has not been fully incorporated in existing approaches in the literature. In this paper, we explicitly incorporate the dependency structure present in the output noise through lagged values of the observed multivariate signals. We formulate a constrained optimization problem to identify the space spanned by the latent states, and the transition matrices of t...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Iterative and Adjustable Soft List Decoding for Polar Codes**

List decoding of polar codes is an outstanding decoding method, in which a fixed-size list is applied to retain the most probable paths. In general, it is preferable to use a large-size list in the decoding to achieve excellent error-control performance, but this can lead to high complexity. Moreover, the list decoding is still a one-time-pass algorithm with hard-decision outputs, which is not well suited for advanced concatenated coding systems. In this paper, a novel adjustable list decoding is proposed, in which the list size can be adjusted appropriately. Based on the reliability analyses of the decoding list, dynamic ...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**New Optimal $Z$-Complementary Code Sets Based on Generalized Paraunitary Matrices**

The concept of paraunitary (PU) matrices arose in the early 1990 s in the study of multi-rate filter banks. These matrices have found wide applications in cryptography, digital signal processing, and wireless communications. Existing PU matrices are subject to certain constraints on their existence and hence their availability is not guaranteed in practice. Motivated by this, for the first time, we introduce a novel concept, called $Z$-paraunitary (ZPU) matrix, whose orthogonality is defined over a matrix of polynomials with identical degree not necessarily taking the maximum value. We show that there exists an equiva...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**FRI Sensing: Retrieving the Trajectory of a Mobile Sensor From Its Temporal Samples**

In this article, contrary to current research trend which consists of fusing (big) data from many different sensors, we focus on one-dimensional samples collected by a unique mobile sensor (e.g., temperature, pressure, magnetic field, etc.), without explicit positioning information (such as GPS). We demonstrate that this stream of 1D data contains valuable 2D geometric information that can be unveiled by adequate processing—using a high-accuracy Finite Rate of Innovation (FRI) algorithm: “FRI Sensing”. Our key finding is that, despite the absence of any position information, the basic sequence of 1D senso...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Two-Dimensional $Z$-Complementary Array Code Sets Based on Matrices of Generating Polynomials**

The unprecedented elevation of wireless technologies produces the ever-increasing requirement for higher-dimensional array sets with preferable correlation properties and more flexible parameters. In this paper, we investigate new two-dimensional $Z$-complementary array code sets (2D-ZCACSs) with unimodular arrays, which are more preferable in ultra wideband communication system to improve its performance than 2D mutually orthogonal complementary array set. We first introduce a new idea of 2D $Z$-paraunitary (2D-ZPU) matrices by extending our previous concept of 1D-ZPU matrices. Then, we show that there exists an equivalen...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Robust Transceiver Design for AF Asymmetric Two-Way MIMO Relaying**

We consider a multiple-input multiple-output (MIMO) asymmetric two-way relay (A-TWR) system, wherein the base station (BS) serves a transmit-only user (TU) and receive-only user (RU). The TU transmits data to the BS and the RU receives data from the BS. Because of the asymmetric nature of A-TWR, the RU experiences interference and necessitates a joint transceiver design at the TU, RU, and relay. The existing literature has designed such transceivers by assuming perfect channel state information (CSI) at all the nodes. Obtaining perfect CSI at all the nodes is, however, not viable in practice due to the distributed nature o...

**Source: **IEEE Transactions on Signal Processing - October 9, 2020 **Category: **Biomedical Engineering **Source Type: **research

**On the Inclusion and Utilization of Pilot Tones in Unique Word OFDM**

Unique word-orthogonal frequency division multiplexing (UW-OFDM) is known to provide various performance benefits over conventional OFDM using cyclic prefixes (CP). Most important, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio performance. Current research has mainly focused on principle performance bounds of UW-OFDM, with less attention on challenges aside from idealized communication scenarios, such as system parameter estimation tasks. In this work we present an approach for including frequency pilot tones into the UW-OFDM signaling scheme, which can then be utili...

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Erratum to “Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing”**

(Source: IEEE Transactions on Signal Processing)

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Random Phaseless Sampling for Causal Signals in Shift-Invariant Spaces: A Zero Distribution Perspective**

We proved that the phaseless sampling (PLS) in the linear-phase modulated shift-invariant space (SIS) $V(e^{{bf i}alpha cdot }varphi), alpha ne 0,$ is impossible even though the real-valued function $varphi$ enjoys the full spark property (so does $e^{{bf i}alpha cdot }varphi$). Stated another way, the PLS in the complex-generated SISs is essentially different from that in the real-generated ones. Motivated by this, we first establish the condition on the complex-valued generator $phi$ such that the PLS of nonseparable causal (NC) signals in $V(phi)$ can be achieved by random sampling. The condition is established from the...

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series**

Gaussian processes (GPs) for machine learning have been studied systematically over the past two decades. However, kernel design for GPs and the associated hyper-parameters optimization are still difficult, and to a large extent open problems. We consider GP regression for time series modeling and analysis. The underlying stationary kernel is approximated closely by a new grid spectral mixture (GSM) kernel, which is a linear combination of low-rank sub-kernels. In the case where a large number of the involved sub-kernels are used, either the Nyström or the random Fourier feature approximations can be adopted to reduce...

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Cramér–Rao Bounds for Complex-Valued Independent Component Extraction: Determined and Piecewise Determined Mixing Models**

Blind source extraction (BSE) aims at recovering an unknown source signal of interest from the observation of instantaneous linear mixtures of the sources. This paper presents Cramér-Rao lower bounds (CRLB) for the complex-valued BSE problem based on the assumption that the target signal is independent of the other signals. The target source is assumed to be non-Gaussian or non-circular Gaussian while the other signals (background) are circular Gaussian or non-Gaussian. The results confirm some previous observations known for the real domain and yield new results for the complex domain. Also, the CRLB for independen...

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Distributed Multi-Sensor Fusion of PHD Filters With Different Sensor Fields of View**

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are running locally in every sensor for MTT. The weighted arithmetic average (WAA) fusion rule is employed to fuse the multiple local PHD densities due to its computational efficiency. First, we provide a theoretical analysis showing that the standard WAA fusion among sensors with different FoVs is unsuitable from the perspective of the principle of minimum discrimination information (PMDI). In fact, the information inconsistency among sensor...

**Source: **IEEE Transactions on Signal Processing - October 6, 2020 **Category: **Biomedical Engineering **Source Type: **research

**An Investigation and Solution of Angle Based Rigid Body Localization**

This paper investigates the problem of rigid body localization using angle measurements between the sensors on the body, and some anchors. Rigid body localization consists of the estimation of the rotation, and position of the rigid body, where the sensor positions are known locally to the body. We have shown that a minimum of one sensor is sufficient for the rigid body to self-localize itself with respect to its own local coordinate system. On the other hand, as little as one anchor can locate the rigid body with respect to the global coordinate system. This is in contrast to the previous studies for range based rigid bod...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Practical Dynamic SC-Flip Polar Decoders: Algorithm and Implementation**

SC-Flip (SCF) is a low-complexity polar code decoding algorithm with improved performance, and is an alternative to high-complexity (CRC)-aided SC-List (CA-SCL) decoding. However, the performance improvement of SCF is limited since it can correct up to only one channel error ($omega =1$). Dynamic SCF (DSCF) algorithm tackles this problem by tackling multiple errors ($omega geq 1$), but it requires logarithmic and exponential computations, which make it infeasible for practical applications. In this work, we propose simplifications and approximations to make DSCF practically feasible. First, we reduce the transcendental com...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Perturbed Amplitude Flow for Phase Retrieval**

In this paper, we propose a new non-convex algorithm for solving the phase retrieval problem, i.e., the reconstruction of a signal $ {mathbf x}in {mathbb {H}}^n$ (${mathbb {H}}={mathbb {R}}$ or ${mathbb {C}}$) from phaseless samples $ b_j=vert langle {mathbf a}_j, {mathbf x}rangle vert $, $ j=1,ldots,m$. The proposed algorithm solves a new proposed model, perturbed amplitude-based model, for phase retrieval, and is correspondingly named as Perturbed Amplitude Flow (PAF). We prove that PAF can recover $c{mathbf x}$ ($vert cvert = 1$) under $mathcal {O}(n)$ Gaussian random measurements (optimal order of measurements). Starti...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Convolutional Beamspace for Linear Arrays**

A new beamspace method for array processing, called convolutional beamspace (CBS), is proposed. It enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure and the shift-invariance are preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better ...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Uniform RIP Conditions for Recovery of Sparse Signals by $ell _p,(0< pleq 1)$ Minimization**

Compressed sensing in both noiseless, and noisy cases is considered in this article, and uniform restricted isometry property (RIP) conditions for sparse signal recovery are established via $ell _p,(00$ for any given constant $t>1$, where $Phi (p,t)$ concerning the restricted isometry constants $delta _{tk}$, and $delta _{2(t-1)k}$ is specified in the context, then all $k$-sparse signals can be exactly recovered by the constrained $ell _p$ minimization. This uniform RIP framework with general $p$, and $t$ includes three state-of-the-art results concerning $p=1$, $t=2$, and $tin [frac{4}{2+p},2]$ as special cases. Utiliz...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Collaborative Sequential State Estimation Under Unknown Heterogeneous Noise Covariance Matrices**

We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm....

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Exact and Robust Reconstructions of Integer Vectors Based on Multidimensional Chinese Remainder Theorem (MD-CRT)**

The robust Chinese remainder theorem (CRT) has been recently proposed for robustly reconstructing a large nonnegative integer from erroneous remainders. It has found many applications in signal processing, including phase unwrapping, and frequency estimation under sub-Nyquist sampling. Motivated by the applications in multidimensional (MD) signal processing, in this article we propose the MD-CRT and robust MD-CRT for integer vectors. Specifically, by rephrasing the abstract CRT for rings in number-theoretic terms, we first derive the MD-CRT for integer vectors with respect to a general set of integer matrix moduli, which p...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Performance of Analog Beamforming Systems With Optimized Phase Noise Compensation**

Millimeter-wave and Terahertz frequencies, while promising high throughput and abundant spectrum, are highly susceptible to hardware non-idealities like phase-noise, which degrade the system performance and make transceiver implementation difficult. While several phase-noise compensation techniques have been proposed, there are limited results on the post-compensation system performance. Consequently, in this paper, a generalized reference-signal (RS) aided low-complexity phase-noise compensation technique is proposed for high-frequency, multi-carrier systems. The technique generalizes several existing solutions and involv...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals**

An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical approach in exploiting the special (Toeplitz) structure of the observations’ covariance. However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in orde...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Model-Based Deep Learning for One-Bit Compressive Sensing**

In this work, we consider the problem of one-bit deep compressive sensing from both a system design and a signal recovery perspective. In particular, we develop hybrid model-based deep learning architectures based on the deep unfolding methodology. We further interpret the overall data-acquisition and signal recovery modules as an auto-encoder structure allowing for learning task-specific sensing matrix, quantization thresholds, as well as the latent-parameters of iterative first-order optimization algorithms specifically designed for the problem of one-bit sparse signal recovery. The proposed model-based deep architecture...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Large Intelligent Surface Aided Physical Layer Security Transmission**

In this paper, we investigate a large intelligent surface-enhanced (LIS-enhanced) system, where a LIS is deployed to assist secure transmission. Our design aims to maximize the achievable secrecy rates in different channel models, i.e., Rician fading, and (or) independent, and identically distributed Gaussian fading for the legitimate, and eavesdropper channels. In addition, we take into consideration an artificial noise-aided transmission structure for further improving system performance. The difficulties of tackling the aforementioned problems are the structure of the expected secrecy rate expressions, and the non-conve...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Learning Proximal Operator Methods for Nonconvex Sparse Recovery with Theoretical Guarantee**

Sparse recovery has attracted considerable attention in signal processing community these years, because of its widespread usage in many applications. Though lots of convex and nonconvex methods have been proposed for this topic, most of them are computationally expensive. Recently, learned iterative shrinkage-thresholding algorithm (LISTA) and its variants are incorporated to sparse recovery, which are based on specific deep neural networks designed by unfolding ISTA. However, the problem they are able to solve is regularized by $ell _1$ norm, which is less effective at promoting sparseness than some nonconvex sparsity-in...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**On DoA Estimation for Rotating Arrays Using Stochastic Maximum Likelihood Approach**

The flexibility needed to construct DoA estimators that can be used with rotating arrays subject to rapid variations of the signal frequency is offered by the stochastic maximum likelihood approach. Using a combination of analytic methods and Monte Carlo simulations, we show that for low and moderate source correlations the stochastic maximum likelihood estimator that assumes noncorrelated sources has accuracy comparable to the estimator that includes the correlation coefficient as one of the parameters. We propose several fast approximations of the stochastic maximum likelihood estimator and compare their accuracy with th...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Radio Transient Detection in Radio Astronomical Arrays**

Celestial transient radio sources have attracted considerable scientific interest recently, but their investigation is hampered by the fact that they cannot be effectively detected by commonly used radio astronomy signal processing techniques. One significant obstacle to observing radio transients is intermittent terrestrial radio frequency interference, which can appear as a transient signal. In this article we present a generalized likelihood ratio test detector for near field sources, such as terrestrial interferences, for which no prior knowledge about the steering vector is assumed. The proposed detector has the desir...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Deviance Tests for Graph Estimation From Multi-Attribute Gaussian Data**

We consider the problem of inferring the conditional independence graph (CIG) of Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. For single-attribute graphical models, considerable body of work exists where one first tests for exclusion of each edge from the saturated model, and then infers the CIG. In this paper, we propose and analyze a deviance test based on generalized likelihood ratio, for edge exclusion in m...

**Source: **IEEE Transactions on Signal Processing - October 2, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Stereo Acoustic Echo Cancellation Based on Maximum Likelihood Estimation With Inter-Channel-Correlated Echo Compensation**

This paper presents batch and online algorithms of a stereo acoustic echo cancellation (SAEC) method. In SAEC, the non-uniqueness problem causes performance degradation, especially for highly coherent far-end signals. In our method, this problem can be avoided without an additional decorrelation preprocessor or multi-microphones by overestimating far-end echoes and compensating for the overestimated inter-channel-correlated echo to obtain a desired echo-canceled signal. In addition, our method is based on the maximum likelihood estimation (MLE) criterion of the echo-canceled signal under the assumption that the signal in t...

**Source: **IEEE Transactions on Signal Processing - September 25, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Robust Adaptive Beamforming Based on Linearly Modified Atomic-Norm Minimization With Target Contaminated Data**

In practice, adaptive beamforming usually faces non-ideal situations where a limited number of snapshots are available, the training data are corrupted by desired target signals, and the array mismatches exist. Traditional methods often degrade significantly under the above situation. In order to solve this problem, a new adaptive beamforming method based on atomic-norm optimization technique is proposed in this paper. In the proposed method, the interference subspace is estimated by minimizing the rank of interference data matrix while making the signals bounded within a ball of Frobenius norm around the observed data. Th...

**Source: **IEEE Transactions on Signal Processing - September 25, 2020 **Category: **Biomedical Engineering **Source Type: **research

**Adaptive Persymmetric Subspace Detectors in the Partially Homogeneous Environment**

This paper addresses the adaptive detection of subspace signals in the noise whose covariance matrix is unknown. The partially homogeneous scenario, where the primary data have the same noise covariance matrix with the training data up to an unknown scaling factor is considered. We exploit the persymmetric structure of the noise covariance matrix to enhance the matched detection performance in the case of limited number of training data. Three persymmetric subspace detectors are proposed by applying the generalized likelihood ratio (GLR), Rao and Wald design criteria, respectively. It is proved that the three persymmetric ...

**Source: **IEEE Transactions on Signal Processing - September 22, 2020 **Category: **Biomedical Engineering **Source Type: **research