IEEE Transactions on Signal Processing IEEE Transactions on Signal Processing RSS feedThis 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.

Online Optimization Under Randomly Corrupted Attacks
Existing algorithms in online optimization usually rely on trustful information, e.g., reliable knowledge of gradients, which makes them vulnerable to attacks. To take into account the security issue in online optimization, this paper investigates the effect of randomly corrupted attacks, which can corrupt gradient information arbitrarily. To conquer the randomly corrupted attack, an onLine muLtiple normaLized Gradient Descent (L3GD) algorithm is proposed. Under mild conditions, the algorithm is proven to achieve satisfactory expected dynamic regret, i.e, $\mathcal{O}(\min\{P_{T}^{*}+T^{\frac{3}{4}},S_{T}^{*}+\sqrt{T}+\sum...
Source: IEEE Transactions on Signal Processing - April 23, 2024 Category: Biomedical Engineering Source Type: research

Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach
In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation and wideband beamsplit, an uncharted domain in existing literature. Our approach to maximizing secrecy rates involves formulating an optimization problem for joint power allocation and analog beamformer design, employing a ...
Source: IEEE Transactions on Signal Processing - April 17, 2024 Category: Biomedical Engineering Source Type: research

Source Enumeration Utilizing Adaptive Diagonal Loading and Linear Shrinkage Coefficients
Source enumeration is typically studied under the assumption of white noise, which may not be suitable for real-world applications. In this article, a source enumeration algorithm robust against both white and colored noises is presented, where the adaptive diagonal loading (ADL) technique combined with linear shrinkage (LS) coefficients are employed. First, a proper loading level is adaptively determined under the presumed number of sources $v$, which is then applied to mitigate correlated and heterogeneous noise, and simultaneously yields an encouraging result that the loaded eigenvalues match the asymptotic ratio behavi...
Source: IEEE Transactions on Signal Processing - April 11, 2024 Category: Biomedical Engineering Source Type: research

Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors
The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements, such as inertial sensors, need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure before being employed. Most of the research over the past years has focused on delivering methods that can explain and estimate the complex stochastic components of these errors. In this context, the Generalized Method of Wavelet Moments emerges as a computationally efficient estimator w...
Source: IEEE Transactions on Signal Processing - April 10, 2024 Category: Biomedical Engineering Source Type: research

BayGO: Decentralized Bayesian Learning and Information-Aware Graph Optimization Framework
Multi-agent Decentralized Learning (MADL) is a scalable approach that enables agents to learn based on their local datasets. However, it presents significant challenges related to the impact of dataset heterogeneity and the communication graph structure on learning speed, as well as the lack of a robust method for quantifying prediction uncertainty. To address these challenges, we propose BayGO, a novel fully-decentralized multi-agent local Bayesian learning with local averaging, usually referred to as non-Bayesian social learning, together with graph optimization framework. Within BayGO, agents locally learn a posterior d...
Source: IEEE Transactions on Signal Processing - April 10, 2024 Category: Biomedical Engineering Source Type: research

Variance Reduced Random Relaxed Projection Method for Constrained Finite-Sum Minimization Problems
For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In this paper, we devise a new random projection method (RPM) to efficiently solve this problem. Compared with existing RPMs, our proposed algorithm features two useful algorithmic ideas. First, at each iteration, instead of projecting onto the set defined by one of the constraints, our algorithm only requires projecting onto a half-space approximation of the set, which significantly reduces the computational cost as it admits a closed-form formula. ...
Source: IEEE Transactions on Signal Processing - April 9, 2024 Category: Biomedical Engineering Source Type: research

Distributed Policy Gradient for Linear Quadratic Networked Control With Limited Communication Range
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems. The agents engage within a specified network under local communication constraints, implying that each agent can only exchange information with a limited number of neighboring agents. On the underlying graph of the network, each agent implements its control input depending on its nearby neighbors’ states in the linear quadratic control setting. We show that it is possible to approximate the exact gradient only using local information. Compared with the ce...
Source: IEEE Transactions on Signal Processing - April 8, 2024 Category: Biomedical Engineering Source Type: research

Distributed Inference With Variational Message Passing in Gaussian Graphical Models: Tradeoffs in Message Schedules and Convergence Conditions
Message passing algorithms on graphical models offer a low-complexity and distributed paradigm for performing marginalization from a high-dimensional distribution. However, the convergence behaviors of message passing algorithms can be heavily affected by the adopted message update schedule. In this paper, we focus on the variational message passing (VMP) applied to Gaussian graphical models and its convergence under different schedules is analyzed. In particular, based on the update equations of VMP under the mean-field assumption, we prove that the mean vectors obtained from VMP are the exact marginal mean vectors under ...
Source: IEEE Transactions on Signal Processing - April 8, 2024 Category: Biomedical Engineering Source Type: research

Non-Uniform Array and Frequency Spacing for Regularization-Free Gridless DOA
Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied in acoustics source localization problems. We formulate this as an atomic norm minimization (ANM) problem and derive an equivalent regularization-free semi-definite program (SDP) thereby avoiding regularization bias. The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP. We also propose a fast SDP program to deal with non-uniform array and frequency spacing. For non-uniform spacings, the Toeplitz structure will not exist, but the DOA is retrieved via irregular Vandermonde dec...
Source: IEEE Transactions on Signal Processing - April 8, 2024 Category: Biomedical Engineering Source Type: research

Neural Augmented Kalman Filtering With Bollinger Bands for Pairs Trading
Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pairwise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity and latency using a Kalman Filter (KF), which are then processed using classic policies such as Bollinger Bands (BB). However, such SS models are inherently approximated and mismatched, often degrading the revenue. In this work, we propose KalmanNet-aided Bollinger band...
Source: IEEE Transactions on Signal Processing - April 8, 2024 Category: Biomedical Engineering Source Type: research

Detection of Ghost Targets for Automotive Radar in the Presence of Multipath
Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with a relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows for the formation of a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex ...
Source: IEEE Transactions on Signal Processing - April 4, 2024 Category: Biomedical Engineering Source Type: research

Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach
The continuously growing size of deep neural networks (DNNs) has sparked a surge in research on model compression techniques. Among these techniques, multi-resolution model compression has emerged as a promising approach which can generate multiple DNN models with shared weights and different computational complexity (resolution) through a single training. However, in most existing multi-resolution compression methods, the model structures for different resolutions are either predefined or uniformly controlled. This can lead to performance degradation as they fail to implement systematic compression to achieve the optimal ...
Source: IEEE Transactions on Signal Processing - April 2, 2024 Category: Biomedical Engineering Source Type: research

Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM
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. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vect...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research

Spatial Registration of Heterogeneous Sensors on Mobile Platforms
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 (active or passive) platforms, this paper proposes a moving heterogeneous sensor registration (MDSR) algorithm based on maximum likelihood estimation. The MDSR algorithm decouples the offset biases from the attitude biases and up...
Source: IEEE Transactions on Signal Processing - April 1, 2024 Category: Biomedical Engineering Source Type: research

DANSE: Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Learning Setup
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 of the state. In addition, it provides a closed-form posterior for forecasting. A data-driven recurrent neural network (RNN) is used in DANSE to provide the parameters of a prior of the state. The prior depends on the past m...
Source: IEEE Transactions on Signal Processing - March 29, 2024 Category: Biomedical Engineering Source Type: research