This paper addresses the problem of frequency diverse array (FDA) signal design for target localization in both non-cognitive and cognitive radars. For the non-cognitive case, the Cramér-Rao bound (CRB) for target localization in FDA radar is derived and optimized with respect to the transmit signal parameters. It is shown that FDA transmission introduces coupling between range and direction-of-arrival

Consider a Bayesian binary decision-making problem in star networks, where local agents make selfish decisions independently, and a fusion agent makes a final decision based on aggregated decisions and its own private signal. In particular, we assume all agents have private beliefs for the true prior probability, based on which they perform Bayesian decision making. We focus on the Bayes risk of the

In this paper, we investigate a nonconvex fractional quadratically constrained quadratic problem (fractional QCQP), which has a wide application to the resource allocation optimization in wireless communication systems. Different from the state-of-the-art methods needing to solve semidefinite programmings or second-order cone programmings, we propose a fast algorithm for solving fractional QCQP by

In this paper, a tensor-based joint channel parameter estimation and information symbol detection scheme is developed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems. At the base station (BS), the information symbols are encoded according to the Khatri-Rao space-time (KRST) method and transmitted through time-varying channels. The received signals at

Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high performance sensing by probing with multiple orthogonal waveforms. However, implementing a low cost low power MIMO radar is challenging. One of the reasons for this difficulty stems from the increased cost and power consumption

Multiobject tracking provides situational awareness that enables new applications for modern convenience, public safety, and homeland security. This paper presents a factor graph formulation and a particle-based sum-product algorithm (SPA) for scalable detection and tracking of extended objects. The proposed method dynamically introduces states of newly detected objects, efficiently performs probabilistic

Federated averaging (FedAvg) is a popular federated learning (FL) technique that updates the global model by averaging local models and then transmits the updated global model to devices for their local model update. One main limitation of FedAvg is that the average-based global model is not necessarily better than local models in the early stage of the training process so that FedAvg might diverge

Classical quantizer design approaches using the Lloyd-Max algorithm (or $k$ -means) have served signal processing applications for more than three decades. With the advent of distributed signal processing and machine learning at edge devices, novel alternatives for quantizers design will be desired to address the energy, communication and hardware constraints. To address these resource challenges,

In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising. Total variation (TV) regularization is adopted in many image processing applications to preserve the local smoothness. However, TV prior is prone to oversmoothness, staircasing effect, and contrast losses. Nonlocal TV (NLTV) mitigates the contrast losses by adaptively weighting the smoothness

The finite impulse response (FIR) approach is known to be more robust than the Kalman approach. In this paper, we derive a batch $q$ -lag maximum likelihood (ML) FIR smoother for full covariance matrices and represent it with an iterative algorithm using recursions for diagonal covariance matrices. It is shown that, under ideal conditions of fully known model, the ML FIR smoother occupies an intermediate

There are various inverse problems – including reconstruction problems arising in medical imaging - where one is often aware of the forward operator that maps variables of interest to the observations. It is therefore natural to ask whether such knowledge of the forward operator can be exploited in deep learning approaches increasingly used to solve inverse problems. In this paper, we provide one such

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In this paper, we formulate and solve a two-stage Bayesian sequential change diagnosis (SCD) problem. Different from the one-stage sequential change diagnosis problem considered in the existing work, after a change has been detected, we can continue to collect low-cost samples so that the post-change distribution can be identified more accurately. The goal of a two-stage SCD rule is to minimize the

Quantization of signals is an integral part of modern signal processing applications, such as sensing, communication, and inference. While signal quantization provides many physical advantages, it usually degrades the subsequent estimation performance that is based on quantized data. In an attempt to maintain physical constraints, while, simultaneously, to attain substantial performance gain, we consider

Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in resolving uncorrelated source signals. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has emerged as an important topic in array processing, as it offers both a low-cost and

As neural networks get widespread adoption in resource-constrained embedded devices, there is a growing need for low-power neural systems. Spiking Neural Networks (SNNs) are emerging to be an energy-efficient alternative to the traditional Artificial Neural Networks (ANNs) which are known to be computationally intensive. From an application perspective, as federated learning involves multiple energy-constrained

Feature selection by maximizing high-order mutual information between the selected feature vector and a target variable is the gold standard in terms of selecting the best subset of relevant features that maximizes the performance of prediction models. However, such an approach typically requires knowledge of the multivariate probability distribution of all features and the target, and involves a challenging

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measurement matrix, which may cause AMP to diverge. Damped AMP has been used for SBL to alleviate divergence issues at the cost of reducing convergence speed. In this work, we propose a new SBL algorithm based on structured variational

Federated Learning (FL) is popular for communication-efficient learning from distributed data. To utilize data at different clients without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a computation then aggregation model, in which multiple local updates are performed using local data before aggregation. These algorithms fail to work when faced with practical

We investigate mismatched data detection for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems in which the prior distribution of the transmit signal used in the data detector differs from the true prior. In order to minimize the performance loss caused by the prior mismatch, we include a tuning stage into the recently proposed large-MIMO approximate message passing (LAMA)

In this paper, a novel recursive realization method with word length reduced is proposed to synthesize linear-phase FIR filters for arbitrary bandwidth. By exploring the sparsity in the frequency domain, it constructs the desired impulse response with few complex exponential series (CES) and very sparse additional coefficients (AC) with equal length. The design problem is formulated as a combinatorial

A 2004 paper had offered a theoretical proof that ideally the roots of the minimum variance distortionless response (MVDR) beamformer array polynomial lie on the unit circle (UC). However, existing MVDR methods fail to exploit this fundamental property adequately. This paper proposes a new adaptive beamforming design via UC roots optimization for uniform linear arrays (ULA). The proposed method starts

Accurate position determination of a wireless device by time-of-arrival (TOA) and angle-of-arrival (AOA) estimation of the line-of-sight (LOS) path with an array of collocated antennas, is important in various applications. In an indoor environment, there can be many multipath reflections, from different angles of arrivals; and, when the signal bandwidth is relatively narrow, the received signal from

Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function

In this paper, we develop a general framework of multi-frequency sparse array to estimate the direction-of-arrival (DOA) of a significantly higher number of targets than the number of physical sensors. The multi-frequency sparse arrays are designed to offer zero lag redundancy in the rendered difference coarray so that the available degrees of freedom are fully utilized to enable high-resolution DOA

Reinforcement learning is a framework to optimize an agent’s policy using rewards that are revealed by the system as a response to an action. In its standard form, reinforcement learning involves a single agent that uses its policy to accomplish a specific task. These methods require large amounts of reward samples to achieve good performance, and may not generalize well when the task is modified,

Modeling uncertainties for real linear systems are unavoidable. These uncertainties can significantly degrade the performance of optimal state estimators designed for nominal system models. The challenge is quantifying such uncertainties and devising robust estimators that are insensitive to them. This paper is therefore concerned with distributionally robust state estimation for linear Markov systems

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and the filtered and smoothed state distributions lack closed-form solutions. As such, it is common to approximate

Coherent frequency diverse array (FDA) radar is able to cover all directions with a stable gain by transmitting a single frequency-shifted waveform. However, it is revealed in this work that the range resolution scales linearly with the number of elements, which is limited by the fixed element number. By leveraging the relationship between the beam direction and the signal frequency, we link the sub-bands

This paper studies the joint estimation of velocity, AOA, and range (JEVAR) of a target in a multipath environment, which has gain renewed interest with the advent of 5G Internet of Things (IoT) technologies, owing to the numerous emerging localization-related applications. To solve the JEVAR problem, we propose a novel scheme, wherein the target transmits a pair of conjugate Zadoff-Chu (ZC) sequences

Maximal correlation measures the statistical dependence between two random variables and has been broadly used by statisticians. In this paper, we present a novel regression framework based on a special case of the maximal correlation using the correlation ratio to train a Multiple-input Multiple-output (MIMO) model called the Bank of Wiener Models (BWM). Nonlinear time series modeling under maximal

We investigate the problem of localizing multiple targets using a single set of measurements from a network of radar sensors. Such “single snapshot imaging” provides timely situational awareness, but can utilize neither platform motion, as in synthetic aperture radar, nor track targets across time, as in Kalman filtering and its variants. Associating measurements with targets becomes a fundamental

The widely-linear complex-valued least-mean-square (WL-CLMS) algorithm exhibits slow convergence in the presence of non-circular and highly correlated filter input signals. To tackle such an issue with reduced computational complexity, this paper introduces adaptive frequency-domain normalized implementations of widely-linear complex-valued filter. Two normalized algorithms are firstly devised based

This paper addresses the problem of selecting an optimal sampling set for $K$ -bandlimited signals on graphs. The proposed sampling method is based on two proposed concepts of correlation quantity and effective node. First, we clarify the relationship between the uniqueness set and the effective node, and subsequently show that the effective node set selected by space division constitutes a uniqueness

Recovery of an unknown sparse signal from a few of its projections is the key objective of compressed sensing. Often one comes across signals that are not ordinarily sparse but are sparse blockwise. Existing block sparse recovery algorithms like BOMP make the assumption of uniform block size and known block boundaries, which are, however, not very practical in many applications. This paper addresses

We study joint nonlinear state estimation with multi-period measurement vectors that are potentially corrupted by sparse gross errors. We consider a nonlinear sparse optimization formulation for joint sparse error correction and robust state estimation in a nonlinear sensing system wherein we exploit the sparsity and short-term invariance properties of gross error locations. We introduce a sequential

This paper is concerned with the Tobit Kalman fusion estimation problem for a class of multi-sensor systems subject to parametric uncertainties and measurement censoring. The parametric uncertainty is characterized by the multiplicative noise appearing in both state and observation equations, and the measurement censoring is governed by the Tobit observation model. The fusion estimation is implemented

In this paper, we investigate the spectral coexistence between a multiple-input multiple-output (MIMO) communication system and a MIMO radar which inspects multiple range gates with non-homogeneous interference. We consider two formulations for joint resource allocation, aiming at minimizing the inverse of the harmonic mean of the signal-to-interference-plus noise ratio (SINR) across the range gates

Turing computability deals with the question of what is theoretically computable on a digital computer, and hence is relevant whenever digital hardware is used. In this paper we study different possibilities to define computable bandlimited signals and systems. We consider a definition that uses finite Shannon sampling series as approximating functions and another that employs computable continuous

We consider diffusion fields induced by multiple localised and instantaneous sources. We assume a mobile sensor samples the field, uniformly along a piecewise linear trajectory, which is unknown. The problem we address is the estimation of the amplitudes and locations of the diffusion sources, as well as of the trajectory of the sensor. We first propose a method for diffusion source localisation and

This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non-smooth). For this non-smooth setting, centralized algorithms are known to converge linearly under certain conditions. On the other hand, decentralized

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite

The causality analysis of multivariate time series and formation of complex networks relies on the estimation of the direct cause-effect from one observed variable to another accounting for the presence of other observed variables. Such effects are quantified by the conditional Granger causality index (CGCI), derived by linear vector autoregressive models (VAR). Dimension reduction approaches have

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. Our method is a model-aware data-driven architecture based on deep unfolding

In this paper, we focus on learning product graphs from multi-domain data. We assume that the product graph is formed by the Cartesian product of two smaller graphs, which we refer to as graph factors. We pose the product graph learning problem as the problem of estimating the graph factor Laplacian matrices. To capture local interactions in data, we seek sparse graph factors and assume a smoothness

The estimation of covariance matrices of multiple classes with limited training data is a difficult problem. The sample covariance matrix (SCM) is known to perform poorly when the number of variables is large compared to the available number of samples. In order to reduce the mean squared error (MSE) of the SCM, regularized (shrinkage) SCM estimators are often used. In this work, we consider regularized

This paper considers the massive connectivity with the aid of a reconfigurable intelligent surface (RIS), where an enormous number of devices transmit sporadically to a base station (BS). The RIS establishes favorable signal propagation environments to enhance data transmission in massive connectivity in such a scenario. Nevertheless, the BS needs to detect active devices and estimate channels to support

In this paper, we propose an iterative algorithm to address the nonconvex multi-group multicast beamforming problem with quality-of-service constraints and per-antenna power constraints. We formulate a convex relaxation of the problem as a semidefinite program in a real Hilbert space, which allows us to approximate a point in the feasible set by iteratively applying a bounded perturbation resilient

Phase retrieval (PR) is an important component in modern computational imaging systems. Recent advances in deep learning have introduced new possibilities for a robust and fast PR. An emerging technique called deep unfolding provides a systematic connection between conventional model-based iterative algorithms and modern data-based deep learning. Unfolded algorithms, which are powered by data learning

In this paper, we study the channel estimation problem in correlated massive multiple-input-multiple-output (MIMO) systems with a reduced number of radio-frequency (RF) chains. Importantly, other than the knowledge of channel correlation matrices, we make no assumption as to the structure of the channel. To address the limitation in the number of RF chains, we employ hybrid beamforming, comprising

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Kernel-basedlearning has well-documented merits in various machine learning tasks. Most of the kernel-based learning approaches rely on a pre-selected kernel, the choice of which presumes task-specific prior information. In addition, most existing frameworks assume that data are collected centrally at batch. Such a setting may not be feasible especially for large-scale data sets that are collected