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Funding Information: This research was supported in part by the National Natural Science Foundation of China under Grant No. 61901136, the National Key Research and Development Program of China under Grant No. 2021YFC2801204, the Ph.D. Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities under Grant No. 3072021GIP0507, and the China Scholarship Council under Grant No. 202006680029. The authors would like to thank the Acoustic Research Laboratory of the National University of Singapore for providing the snapping shrimp noise. The authors also thank Mandar Chitre, the head of the Acoustic Research Laboratory, for his helpful comments to improve the manuscript. Publisher Copyright: © 2022 Acoustical Society of America. Detecting the presence or absence of a known signal is an important aspect of underwater acoustic (UWA) communications as it is a vital first step to process the received data. The influence of impulsive noise and multipath propagation on detection are both considered in this paper. Many robust detectors have been designed to cope with impulsive noise, but they tend to ignore the impact of multipath propagation, which may decrease the detection probability. To emphasize the role of multipath channel, we propose two-stage detection methods. In the first stage, the channel is estimated based on robust orthogonal matching pursuit, while in the second stage, detectors that use the channel estimation from the first stage, namely augmented log-likelihood ratio detector and augmented pseudo-correlation detector, are developed. The former detector is parametric and the latter one is non-parametric. The improved performance is demonstrated in simulated multipath channel and actual UWA channel with both simulated noiseand recorded snapping shrimp noise, indicating that the proposed detectors are resistant to impulsive noise. It is also shown that the use of multipath information helps to improve the detection performance.

We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general 4-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different subarrays. Specifically, the received signal of TB MIMO radar is expressed as a 4-order tensor. Depending on the target Doppler shifts, the constructed tensor is reshaped into two distinct 3-order tensors. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding target DOA. It is further shown that our proposed method can be used in a more general scenario where the subarray structures can be arbitrary but identical. The proposed DOA estimation method requires no prior information about the tensor rank and is guaranteed to achieve precise decomposition result. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to conventional DOA estimation techniques for TB MIMO Radar., 14 pages, 9 figures, submitted to IEEE Transactions on Signal Processing

Publisher Copyright: Author This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.

Publisher Copyright: © 2022 The Author(s) This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods.Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

Publisher Copyright: © 2022 IEEE A novel method for learning correlated features in multimodal images based on convolutional sparse coding with applications to image fusion is presented. In particular, the correlated features are captured as coupled filters in convolutional dictionaries. At the same time, the shared and independent features are approximated using separate convolutional sparse codes and a common dictionary. The resulting optimization problem is addressed using alternating direction method of multipliers. The coupled filters are fused based on a maximum-variance rule, and a maximum-absolute-value rule is used to fuse the sparse codes. The proposed method does not entail any prelearning stage. The experimental evaluations using medical and infrared-visible image datasets demonstrate the superiority of our method compared to state-of-the-art algorithms in terms of preserving the details and local intensities as well as improving objective metrics.

Publisher Copyright: © 2022 The Author(s) A plethora of problems arising in signal processing, machine learning and statistics can be cast as large-scale optimization problems with a composite objective structure. Such problems are typically solved by utilizing iterative first-order algorithms. In this work, we devise a new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite objective structure. Specifically, we introduce a new class of estimating functions, which are obtained by utilizing both a tight lower bound on the objective function, as well as the gradient mapping technique. Then, using the proposed estimating functions, we construct a class of Composite Objective Multi-step Estimating-sequence Techniques (COMET), which are endowed with an efficient line-search procedure. We prove that our proposed COMET enjoys the accelerated convergence rate, and our newly established convergence results allow for step-size adaptation. Our theoretical findings are supported by extensive computational experiments on various problem types and real-world datasets. Moreover, our numerical results show evidence of the robustness of the proposed method to the imperfect knowledge of the smoothness and strong convexity parameters.

Publisher Copyright: © IEEE Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem. The only major difference between these methods is how they approach a convolutional least-squares fitting subproblem. In this letter, we present a novel solution for this subproblem, which improves the computational efficiency of the existing algorithms. The same approach is also used to develop an efficient dictionary learning method. In addition, we propose a novel algorithm for convolutional sparse coding with a constraint on the approximation error. Source codes for the proposed algorithms are available online.

Publisher Copyright: Author This paper studies the impact of additional pilot overhead for covariance matrix estimation in a time-division duplexed (TDD) massive multiple-input multiple-output (MIMO) system. We choose average uplink (UL) and downlink (DL) spectral efficiencies (SEs) as performance metrics for the massive MIMO system, and derive closed form expressions for them in terms of the additional pilot overhead. The expressions are derived by considering linear minimum mean squared error (LMMSE)-type and element-wise LMMSE-type channel estimates that represent LMMSE and element-wise LMMSE with estimated covariance matrices, respectively. Using these expressions, a detailed theoretical analysis of SE behavior as a function of pilot overhead for both LMMSE-type and element-wise LMMSE-type channel estimation are presented, followed by simulations, which also demonstrate and validate theoretical results.

Funding Information: This work was supported by the ERC Grant 692891-DAMOCLES, the Academy of Finland Flagship funding (grant no. 337549), the University of Helsinki, Faculty of Science ATMATH project, and the National Center of Meteorology (NCM), Abu Dhabi, UAE, under the UAE Research Program for Rain Enhancement Science. Supercomputing resources were provided by CSC–IT Center for Science, Ltd., Finland. B.R. is grateful to Dr. Stephen Ingram for his advice on setting up the antifreeze protein simulations. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Center of Meteorology, Abu Dhabi, UAE, funder of the research. Publisher Copyright: © 2021 American Chemical Society. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. We propose a novel method based on template matching for the recognition of liquid water, cubic ice (ice Ic), hexagonal ice (ice Ih), clathrate hydrates, and different interfacial structures in atomistic and coarse-grained simulations of water and ice. The two template matrices represent staggered and eclipsed conformations, which are the building blocks of hexagonal and cubic ice and clathrate crystals. The algorithm is rotationally invariant and highly robust against imperfections in the ice structure, and its sensitivity for recognizing ice-like structures can be tuned for different applications. Unlike most other algorithms, it can discriminate between cubic, hexagonal, clathrate, mixed, and other interfacial ice types and is therefore well suited to study complex systems and heterogeneous ice nucleation.

We analyze the performance of amplify-and-forward (AF) automatic repeat request (ARQ) for a two-hop cooperative system with reliability constrains. For this setup, we first derive the closed-form outage probability expression. Next, we present a power allocation scheme that allows us to achieve a target outage probability, while minimizing the outage-weighted average power expenditure for asymmetric power allocation between the source and relay. This is cast as an optimization problem, and the optimal power allocation (OPA) is obtained in closed form by invoking the Karush-Kuhn-Tucker (KKT) conditions. We evaluate numerically the OPA strategy between different AFARQ transmission rounds and we show that the proposed scheme provides large power gains with respect to the optimized pointto-point ARQ scheme, as well as with respect to the equal power allocation (EPA) strategy.

The image fusion problem consists in combining complementary parts of multiple images captured, for example, with different focal settings into one image of higher quality. This requires the identification of the sharpest areas in sets of input images. Recently, it was shown that coupled dictionary learning can successfully capture the relationships between high- and low-resolution patches in the context of single image super-resolution. In this work, to identify the sharp image patches, we propose an improved discriminative coupled dictionary learning approach using joint sparse representations in blurred and focused dictionaries. In addition, a pixel-wise processing of the boundaries (i.e., patches containing blurred and focused pixels) is proposed. The experimental results using two natural image datasets, as well as a sequence of in vivo microscopy images, show the competitiveness of the proposed method compared to state-of-the-art algorithms in terms of accuracy and computational time.

Generalized sidelobe canceler (GSC) uses a two step procedure in order to produce a beampattern with a fixed mainlobe and suppressed sidelobes. In the first step, a beampattern with a fixed response in the look direction is produced by convolving a vector of constraints with a normalized beamforming vector with the desired mainlobe response. In the second step, the signals in the look direction are blocked out using so-called blocking matrix, while the output power is minimized. Observing that for Griffiths-Jim GSC the beamforming vector contains the coefficients of a polynomial with at least one root at 1, we find here that all rows of a blocking matrix should be the coefficients of polynomials from the polynomial ideal with a root at 1. This allows us to reveal and exploit the underlying algebraic structure for GSC blocking matrix design using methods from computational algebraic geometry. It also allows to arrive to and prove several generalized statements. For example, the necessary and sufficient condition for a signal to be blocked can be easily found. The condition to a row-space of blocking matrix for blocking multiple signals impinging upon the array from multiple directions can also be easily formulated. The linear independence of rows of blocking matrix implies that all the corresponding polynomial share a single root. In general, understanding the algebraic structure that GSC's blocking matrix has to satisfy makes the GSC's design simpler and more intuitive.

Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals. In simulation studies, we show that the randomized greedy robust least mean squares (RGRLMS) outperforms the regular LMS and has even more potential given a robust sampling design.