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.

Stable Remote Optimal Filtering and Fusion With Communication Driven by Cumulative Estimate Innovation
This article studies remote filtering for a discrete-time linear system observed by one or more sensors with limited communication resources. We propose new communication schemes based on cumulative estimate innovation and derive the corresponding minimum mean square error (MMSE) filter and fuser. Our communication schemes balance communication cost and estimation performance. The proposed remote filter and fuser can improve estimation under limited communication resources by leveraging the information of no transmission. Further, it is proved that these filters have guaranteed stability—the expected norms of the mean sq...
Source: IEEE Transactions on Signal Processing - November 6, 2023 Category: Biomedical Engineering Source Type: research

Frequency Domain Joint-Normalized Stochastic Gradient Projection-Based Algorithm for Widely Linear Quaternion-Valued Adaptive Filtering
The widely linear quaternion-valued least-mean-squares (WLQ-LMS) algorithm in the time domain tends to slow down its convergence when dealing with highly correlated or non-circular input signals. To address such a problem while reducing the computational complexity, in this article, we develop an adaptive frequency domain widely linear quaternion-valued joint-normalized stochastic gradient projection-based (FDWLQ-NGP) algorithm, which comprises gradient update and projection steps. In the first step, our joint-normalization design using joint second-order statistical information of ortho-split representation of quaternion ...
Source: IEEE Transactions on Signal Processing - November 3, 2023 Category: Biomedical Engineering Source Type: research

Optimal Power Allocation Under Different Power Availability Scenarios for Multitarget Tracking With C-MIMO Radar Systems
Power allocation has emerged to be a critical problem when exploiting colocated multiple-input and multiple-output (C-MIMO) radar for multi-target tracking. Several prior approaches employing the quality of service-based framework aim to minimize the weighted sum of the target task utility functions. In this article, to utilize power-resource efficiently and further improve the tracking performance of the C-MIMO radar system, an optimal power allocation (OPA) method is proposed. First, the quality of service based power allocation model is generalized to a more general and flexible model, where the task utility functions c...
Source: IEEE Transactions on Signal Processing - November 2, 2023 Category: Biomedical Engineering Source Type: research

Fairness-Aware Regression Robust to Adversarial Attacks
In this paper, we take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. Using a minimax framework, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to add a carefully designed adversarial data point to the dataset or perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. For both synthetic data and real-...
Source: IEEE Transactions on Signal Processing - November 2, 2023 Category: Biomedical Engineering Source Type: research

Bayesian Tensor Tucker Completion With a Flexible Core
Tensor completion is a vital task in multi-dimensional signal processing and machine learning. To recover the missing data in a tensor, various low-rank structures of a tensor can be assumed, and Tucker format is a popular choice. However, the promising capability of Tucker completion is realized only when we can determine a suitable multilinear rank, which controls the model complexity and thus is essential to avoid overfitting/underfitting. Rather than exhaustively searching the best multilinear rank, which is computationally inefficient, recent advances have proposed a Bayesian way to learn the multilinear rank from tra...
Source: IEEE Transactions on Signal Processing - November 2, 2023 Category: Biomedical Engineering Source Type: research

Low-Complexity Symbol Reconstruction Based on Direct Symbol Decision for FBMC-OQAM Systems
In this paper, we propose a series of low-complexity symbol reconstruction methods for filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) systems, where the key is direct symbol decision. Specifically, by analyzing the distribution characteristics of the demodulated OQAM symbols at the receiver, we firstly propose an interference avoidance based direct symbol decision (IAD) method that employs dual pilot symbols, avoiding the interference term and eliminating the need for guard symbols. Then, we observe that the decision coefficients on each subcarrier can be obtained from a straight line, whi...
Source: IEEE Transactions on Signal Processing - November 1, 2023 Category: Biomedical Engineering Source Type: research

Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks From a System Theoretic Viewpoint
Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well known tools for predicting time series. Even though these solutions were developed independently in distinct communities, they share some similarities when considered as probabilistic structures. So in this paper we first consider HMC and RNN as generative models, and we embed both structures in a common generative unified model (GUM). We next address a comparative study of the expressivity (or modeling power) of these models, which here refers to the range of the joint probability distribution of an observations sequence, induced by the underlying...
Source: IEEE Transactions on Signal Processing - November 1, 2023 Category: Biomedical Engineering Source Type: research

Achievable Rates of Generalized Linear Systems With Orthogonal/Vector AMP Receiver
In signal processing and wireless communications, the generalized linear system (GLS) has been widely used to evaluate the impact of nonlinear preprocessing on receiver performance. Although generalized approximation message passing (GAMP) is a state-of-the-art algorithm for GLS signal recovery, it is only applicable to measurement matrices with independent and identically distributed (IID) elements. To overcome this limitation, the generalized orthogonal/vector approximate message passing (GOAMP/GVAMP) algorithm for unitarily invariant measurement matrices was developed and shown to be replica Bayes optimal in uncoded GLS...
Source: IEEE Transactions on Signal Processing - November 1, 2023 Category: Biomedical Engineering Source Type: research

High-Accuracy Positioning Services for High-Speed Vehicles in Wideband mmWave Communications
It is expected that the sixth-generation (6G) cellular networks will provide high-accuracy positioning services. For the millimeter wave (mmWave) frequency band in 6G, both the Doppler and the spatial wideband effects can lead to channel variation in time and space domains, respectively. However, the impact of these effects on the positioning performance is not well studied. In this paper, we will investigate this issue and show that these two effects are not only challenges, but also provide great opportunities in terms of positioning in vehicular networks. Particularly, we will conduct system modeling, algorithm design, ...
Source: IEEE Transactions on Signal Processing - October 31, 2023 Category: Biomedical Engineering Source Type: research

Unsupervised Learning of Sampling Distributions for Particle Filters
Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on their importance weight. For particle filters to be computationally tractable, it must be feasible to simulate the trajectories by drawing from the sampling distribution. Simultaneously, these trajectories need to reflect the reality of the nonlinear dynamical system so that the resulting estimators are accurate. Thus, the crux of particle filters lies in designing sampling distr...
Source: IEEE Transactions on Signal Processing - October 27, 2023 Category: Biomedical Engineering Source Type: research

Causal Fourier Analysis on Directed Acyclic Graphs and Posets
We present a novel form of Fourier analysis, and associated signal processing concepts, for signals (or data) indexed by edge-weighted directed acyclic graphs (DAGs). This means that our Fourier basis yields an eigendecomposition of a suitable notion of shift and convolution operators that we define. DAGs are the common model to capture causal relationships between data values and in this case our proposed Fourier analysis relates data with its causes under a linearity assumption that we define. The definition of the Fourier transform requires the transitive closure of the weighted DAG for which several forms are possible ...
Source: IEEE Transactions on Signal Processing - October 27, 2023 Category: Biomedical Engineering Source Type: research

Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems
Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF systems stems from the fact their application often involves a non-convex, highly complex optimization of large dimensions. In this article, we propose HBF schemes that leverage data to enable efficient designs for both the fully-connected HBF (FC-HBF) and dynamic sub-connected HBF (SC-HBF) architectures. We develop a deep unfolding framework based on factorizing the optimal fully digital beamformer into analog and digital terms and formulating two...
Source: IEEE Transactions on Signal Processing - October 27, 2023 Category: Biomedical Engineering Source Type: research

A Marginal Distributionally Robust MMSE Estimation for a Multisensor System With Kullback-Leibler Divergence Constraints
In this paper, we study a novel marginal distributionally robust minimum mean-squared error (MDR-MMSE) estimation problem for a random state vector in a multisensor system consisting of several sensors linked with a fusion center by employing the minimax viewpoint, which involves finding the estimator with the best performance under the least favorable distribution within an uncertainty set. In contrast to previous studies, the uncertainty set includes a family of densities whose marginal densities are located in a neighborhood defined by placing a threshold on the Kullback-Leibler (KL) divergence between actual and nomina...
Source: IEEE Transactions on Signal Processing - October 27, 2023 Category: Biomedical Engineering Source Type: research

Bayesian Nonparametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis
Multi-function radars are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this article proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this article designed a fully-conjugate Bayesian Non-Parametric Hidden Markov Model with a designed prior distribution (agile BNP-HMM) to ...
Source: IEEE Transactions on Signal Processing - October 26, 2023 Category: Biomedical Engineering Source Type: research

Collaborative Learning in Kernel-Based Bandits for Distributed Users
We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. Each client has direct access to random bandit feedback on its local objective, but only has a partial view of the global objective and relies on information exchange with other clients for collaborative learning. We adopt the kernel-based bandit framework where the objective functions belong to a reproducing kernel Hilbert space. We propose an algorithm based on surrogate Gaussian process (G...
Source: IEEE Transactions on Signal Processing - October 19, 2023 Category: Biomedical Engineering Source Type: research