IEEE Transactions on Signal Processing 
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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.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
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
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
Ultimately Bounded State Estimation for Nonlinear Networked Systems With Constrained Average Bit Rate: A Buffer-Aided Strategy
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 signals when the communication channel is unavailable. A so-called average bit rate constraint is introduced to restrain the transmission rate of the communication channel with the aim of avoiding the communication burden. Th...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research
Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints
We consider scattered data approximation in samplet coordinates with $\ell_{1}$-regularization. The application of an $\ell_{1}$-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 data sets. They provide similar properties as wavelets in terms of localization, multiresolution analysis, and data compression. By using the Riesz isometry, we embed samplets into reproducing kernel Hilbert spaces and discu...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research
A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models
In this paper, we consider the problem of testing equality of the covariance matrices of $L$ complex Gaussian multivariate time series of dimension $M$. We study the special case where each of the $L$ covariance matrices is modeled as a rank $K$ 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 covariance matrices is proposed. In particular, we show that this statistic is consistent and with controlled type I error in the high-dimensional asymptotic regime where the sample sizes $N_{1},\dots,N_{L}$ of each time se...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research
Blind Graph Matching Using Graph Signals
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 first, a process that is highly sensitive to observation errors. In this paper, we tackle the blind graph matching problem with unknown underlying graphs directly using observations of graph signals, which are generated from ...
Source: IEEE Transactions on Signal Processing - March 28, 2024 Category: Biomedical Engineering Source Type: research
Optimal Bayesian Regression With Vector Autoregressive Data Dependency
In this study, we derive a closed-form analytic representation of the optimal Bayesian regression when the data are generated from $\text{VAR}(p)$, which is a multidimensional vector autoregressive process of order $p$. 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 setting $p=0$, which implies independent data. Our empirical results using both synthetic and real data show that the developed regressor can effectively be used in situations where the data are sequentially dependent. (Source: IEEE...
Source: IEEE Transactions on Signal Processing - March 27, 2024 Category: Biomedical Engineering Source Type: research
Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation
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 rarely used in practice because of a lack of available reliable estimation procedures for the vector of selfsimilarity parameters. Building upon recent mathematical developments, the present work puts forth an efficient e...
Source: IEEE Transactions on Signal Processing - March 25, 2024 Category: Biomedical Engineering Source Type: research
Deep Unfolding Transformers for Sparse Recovery of Video
Deep unfolding models are designed by unrolling an optimization algorithm into a deep learning network. By incorporating domain knowledge from the optimization algorithm, they have shown faster convergence and higher performance compared to the original algorithm. We design an optimization problem for sequential signal recovery, which incorporates that the signals have a sparse representation in a dictionary and are correlated over time. A corresponding optimization algorithm is derived and unfolded into a deep unfolding Transformer encoder architecture, coined DUST. To show its improved reconstruction quality and flexibil...
Source: IEEE Transactions on Signal Processing - March 25, 2024 Category: Biomedical Engineering Source Type: research
