Before the execution of many standard graph signal processing (GSP) modules, such as compression and restoration, learning of a graph that encodes pairwise (dis)similarities in data is an important precursor. In data-starved scenarios, to reduce parameterization, previous graph learning algorithms make assumptions in the nodal domain on i) graph connectivity (e.g., edge sparsity), and/or ii) edge weights

Cooperative sensing utilizes multiple receivers dispersed across different locations, capitalizing on the advantages of multiple antennas and spatial diversity gain. This mechanism is crucial for monitoring the availability of licensed spectrum for secondary use when free from primary users. However, the efficacy of cooperative sensing relies heavily on the flawless transmission of raw data from cooperating

In this paper, we address the problem of detecting a Noise-Like Jammer (NLJ) that does not quickly transmit all the available power but it gradually increases the transmitted power. This control strategy would prevent conventional electronic counter-countermeasures from revealing the presence of a noise power discontinuity in the window under test. As a consequence, the radar system under attack becomes

When multiple devices seek to offload computational tasks to their access point, the nature of the multiple access scheme plays a critical role in the system performance. For a system with heterogeneous tasks, we adopt a time-slotted signaling architecture in which different numbers of devices transmit in each slot, subject to individual power constraints. We consider the problem of jointly selecting

This paper addresses the challenge of reconstructing a 3D power spectrum map from sparse, scattered, and incomplete spectrum measurements. It proposes an integrated approach combining interpolation and block-term tensor decomposition (BTD). This approach leverages an interpolation model with the BTD structure to exploit the spatial correlation of power spectrum maps. Additionally, nuclear norm regularization

A fundamental challenge in cognitive radio is the detection of primary users in a licensed spectrum. Cooperative sensing, which utilizes multiple receivers distributed across different locations, offers the advantage of utilizing multiple antennas and achieving spatial diversity gain. However, successful implementation of cooperative sensing relies on the ideal exchange of information among cooperating

In a massive multi-input multi-output (MIMO) cellular communication system, the conventional beam-sweeping scheme for common message broadcasting provides high beamforming gain but requires too many time slots due to the narrowness of the beams. To reduce the beam sweeping time while maintaining a sufficient beamforming gain, this paper focuses on designing broad beams with tunable beamwidths. First

Integrated sensing and communication (ISAC) is emerging as a key technique for next-generation wireless systems. In order to expedite the practical implementation of ISAC in pervasive mobile networks, it is crucial to have widely deployed base stations with radar sensing capabilities. Thus, the utilization of standardized multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing

This paper studies a near-field multiple-input multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space over unknown cluttered environments. We consider a spherical wavefront propagation with both channel phase and amplitude variations over different antennas. Under this setup, the unknown

Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation. While such systems can often be described as state space (SS) models, tracking graph signals via conventional tools based on the Kalman filter (KF) and its variants is typically challenging. This is due to the nonlinearity, high dimensionality, irregularity of the domain

Reconfigurable intelligent surface (RIS) is considered as a promising technology for future 6G communications. In RIS assisted communication systems, precise channel state information (CSI) is the prerequisite of efficient beamforming. Most existing channel estimation schemes rely on the spatial stationarity assumption. However, with the large array aperture of RIS, spatial non-stationarity effect

The fractional Fourier transform (FRFT) parametrically generalizes the Fourier transform (FT) by a transform order, representing signals in intermediate time-frequency domains. The FRFT has multiple but equivalent definitions, including the fractional power of FT, time-frequency plane rotation, hyper-differential operator, and many others, each offering benefits like derivational ease and computational

This paper establishes a mathematical framework to analyze the behavioral utility-based distributed detection problem for $M$ -ary hypothesis testing with conditionally independent observations at the local decision agents (DAs). It is assumed that a human acts as the fusion center (FC) and his subjective perception of probabilities and gains/losses are considered using a prospect theoretic approach

In this paper, a channel state information (CSI)-free, fake path injection (FPI) scheme is proposed for location-privacy preservation. By leveraging the geometrical feasibility of the fake paths, under mild conditions, it can be proved that the illegitimate device cannot distinguish between a fake and true path, thus degrading the illegitimate devices’ ability to localize. Two closed-form, lower bounds

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation. The derived estimator is shown to approximate

A novel private communication framework is proposed where privacy is induced by transmitting over a channel instances of linear inverse problems that are identifiable to the legitimate receiver but unidentifiable to an eavesdropper. The gap in identifiability is created in the framework by leveraging secret knowledge between the transmitter and the legitimate receiver. Specifically, the case where

In the field of signal processing, the sampling theorem plays a fundamental role for signal reconstruction as it bridges the gap between analog and digital signals. Following the celebrated Nyquist-Shannon sampling theorem, generalizing the sampling theorem to non-band-limited signals remains a major challenge. In this work, a generalized sampling theorem, which builds upon the Koopman operator, is

In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems. The proposed approach is based on a constrained maximum likelihood estimator (CMLE) of the complex-valued Laplacian, which is formulated as an optimization problem with Laplacian and sparsity constraints. The complex-valued

In many parameter estimation problems, the exact model is unknown. In such cases, a predetermined data-based selection rule selects a parametric model from a set of candidates before the parameter estimation. The existing framework for estimation under model misspecification does not account for the selection process that led to the misspecified model. Moreover, in post-model-selection estimation,

How to establish control design methods for learning systems that are subjected to the stochastic uncertainties becomes a topic of practical importance in the control field. This paper deals with the stochastic iterative learning control (ILC) problems for linear time-varying systems subject to measurement noises and changing durations. The lengths of the changing durations are modeled as a Markov

Recent work (Ramírez et al., 2020) has introduced Multi-Channel Factor Analysis (MFA) as an extension of factor analysis to multi-channel data that allows for latent factors common to all channels as well as factors specific to each channel. This paper validates the MFA covariance model and analyzes the statistical properties of the MFA estimators. In particular, a thorough investigation of model identifiability

This paper addresses the problem of robust Gaussian mixture modeling in the presence of outliers. We commence by introducing a general expectation-maximization (EM)-like scheme, called $\mathcal{K}$ -BM, for iterative numerical computation of the minimum $\mathcal{K}$ -divergence estimator (M $\mathcal{K}$ DE). This estimator leverages Parzen's non-parametric $\mathcal{K}$ ernel density estimate to

We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this

Motivated by modern data applications such as cryo-electron microscopy, the goal of classic multi-reference alignment (MRA) is to recover an unknown signal $f:\mathbb{R}\to\mathbb{R}$ from many observations that have been randomly translated and corrupted by additive noise. We consider a generalization of classic MRA where signals are also corrupted by a random scale change, i.e. dilation. We propose

In this paper, we consider a distributed passive radar with non-cooperative illuminators of opportunity (IOs) operating on non-overlapping frequency bands and tackle the problem of direct target localization. Assuming that each passive radar receiver employs reference channels to measure the direct-path signals from the IOs and a surveillance channel to collect the target echoes corrupted by the direct-path

Accurate georegistration is required in multi-sensor data fusion, since even minor biases in spatial registration can result in large errors in the converted target geolocation. This paper addresses the problem of estimating and correcting sensor biases in target geolocation. Aiming to solve the spatial registration problem in the case where heterogeneous measurements are provided by mobile sensor

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements

We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori knowledge of the process dynamics. In the article, we propose DANSE – a Data-driven Nonlinear State Estimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements

In this paper, we consider the problem of testing equality of the covariance matrices of LL complex Gaussian multivariate time series of dimension MM. We study the special case where each of the LL covariance matrices is modeled as a rank KK perturbation of the identity matrix, corresponding to a signal plus noise model. A new test statistic based on the estimates of the eigenvalues of the different

This article investigates the state estimation issue for a nonlinear networked system with network-based communication, where the measurement signals of the system are transmitted in an intermittent manner under the effects of unreliable communication. For the sake of enhancing the utilization efficiency of measurement signals, a buffer-aided strategy is employed here by storing historical measurement

Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, the use of conventional graph matching methods requires inferring the graph topologies

We consider scattered data approximation in samplet coordinates with ℓ1\ell_{1}-regularization. The application of an ℓ1\ell_{1}-regularization term enforces sparsity of the coefficients with respect to the samplet basis. Samplets are wavelet-type signed measures, which are tailored to scattered data. Therefore, samplets enable the use of well-established multiresolution techniques on general scattered

In this study, we derive a closed-form analytic representation of the optimal Bayesian regression when the data are generated from VAR(p)\text{VAR}(p), which is a multidimensional vector autoregressive process of order pp. Given the covariance matrix of the underlying Gaussian white-noise process, the developed regressor reduces to the conventional optimal regressor for a non-informative prior and

In machine learning applications, data are often high-dimensional and intricately related. It is often of interest to find the underlying structure and Granger causal relationships among the data and represent these relationships with directed graphs. In this paper, we study multivariate time series, where each series is associated with a node of a graph, and where the objective is to estimate the

In multi-source two-dimensional (2D) direction-of-arrival (DOA) estimation, the essential matching process between the estimated and the true DOAs in the mean square error (MSE) calculation is often based on minimum Euclidean distance criterion, which is substantially different from 1D DOA estimation that is based on simple ordering process. Hence, the ZZB for multi-source 2D DOA estimation is not

In typical spectrometric measurement systems, a high-resolution spectrum is obtained directly via sequential observations with a narrow slit-like measurement window at the expense of sensitivity. In this paper, we propose a novel spectrometric method applicable to these typical spectrometric systems: a multiplexed low-resolution measurement with a wide measurement window, band measurement (BM), is

Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively and ubiquitously used to model temporal dynamics in real-world data. However, its practical use has mostly remained univariate so far. By contrast, modern applications often demand multivariate data analysis. Accordingly, models for multivariate selfsimilarity were recently proposed. Nevertheless, they have remained