This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righthand argument of the KLDs. The optimal fused density following the MIL rule is the linear opinion pool of local ones. However

This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame. A novel rigidity-theory-based distributed

Nonstationary environments are ubiquitous in communications and acoustic systems. The ability to track their dynamics is one of the most desirable features of adaptive processing algorithms. The designers of these algorithms employ guidelines derived from stochastic analyses to adjust user-defined parameters to maximize performance or avoid stability issues. It is therefore important that analyzes

This work studies properties of the conditional mean estimator in vector Poisson noise. The main emphasis is to study conditions on prior distributions that induce linearity of the conditional mean estimator. The paper consists of two main results. The first result shows that the only distribution that induces the linearity of the conditional mean estimator is a product gamma distribution. Moreover

The statements on the BIBO stability of continuous-time convolution systems found in engineering textbooks are often either too vague (because of lack of hypotheses) or mathematically incorrect. What is more troubling is that they usually exclude the identity operator. The purpose of this note is to clarify the issue while presenting some fixes. In particular, we show that a linear shift-invariant

A widely linear quaternion-valued least-mean kurtosis (WL-QLMK) algorithm is introduced for adaptive filtering of quaternion-valued circular and noncircular signals. In the design, kurtosis-based cost function is first defined in the quaternion domain by integrating the widely linear model, and augmented statistics, and then minimized using the recently developed generalized Hamilton-real (GHR) calculus

In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes incoherent linear measurements of the signal, which are represented as separate bitplanes. Each bitplane

In this paper, a variable step-size widely linear complex-valued affine projection algorithm (VSS-WLCAPA) is proposed for processing noncircular signals. The variable step-size (VSS) is derived by minimizing the power of the augmented noise-free a posteriori error vector, which speeds up the convergence and reduces the steady-state misalignment. By exploiting the evolution of the covariance matrix

This paper presents a node-reliability based cyclic redundancy check (CRC)-aided successive cancellation list (NRB CA-SCL) polar decoder architecture, where node reliability is used to determine the number of expanding paths, and a priority-based sorter is devised so as to reduce the number of sorting cycles. In order to reduce the effort required for sorting by the node processor, the expanding segments

In this paper, spectrum efficiency (SE) and energy efficiency (EE) performances of uplink massive multiple-input multiple-output (MIMO) systems with continuous-rate adaptive modulation and imperfect channel state information (CSI) are investigated over Rayleigh fading channels. Given a target BER, closed-form SE and EE expressions are derived. With these results, a constrained non-concave optimization

As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a main challenge limiting the broad use of distributed signal processing techniques is the issue of privacy in handling sensitive data. To address this privacy issue, we propose a novel yet general subspace perturbation method

A multivariate signal denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space $\mathcal {R}^M$ to a single-dimensional

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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 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

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

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 $\ell _p\,(0< p\leq 1)$ minimization. It is shown that if the measurement matrix satisfies the sharp condition $\Phi (p,t)>0$ for any given constant $t>1$ , where $\Phi (p,t)$ concerning the restricted isometry

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

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

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

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

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

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

Sinusoidal frequency-modulated (SFM) signal is widely applied in radar, and wireless communication. Therefore, there is significant importance in studying the estimation of SFM signal parameters. However, traditional estimation algorithms have high computational complexity while the corresponding estimation performance degrades when either modulation index or modulation frequency of SFM signals increases

Low-rank matrix/tensor factorizations play a significant role in science and engineering. An important example is the canonical polyadic decomposition (CPD). There is also a growing interest in multi-set extensions of low-rank matrix/tensor factorizations in which the associated factor matrices are partially shared. In this paper we propose a more unified framework for multi-set matrix/tensor factorizations