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        <title><![CDATA[Signal Processing]]></title>
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        <item>
            <title><![CDATA[A comparative review on multi-modal sensors fusion based on deep learning]]></title>
            <description><![CDATA[<p>
    <span><big>A comparative review on multi-modal sensors fusion based on deep learning</big></span><br>
</p>
<p>
    <span><small><i>Qin Tang, Jing Liang, Fangqi Zhu</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109165</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>The wide deployment of multi-modal sensors in various areas generates vast amounts of data with characteristics of high volume, wide variety, and high integrity. However, traditional data fusion methods face immense challenges when dealing with multi-modal data containing abundant intermodality and cross-modality information. Deep learning has the ability to automatically extract and understand the potential association of multi-modal information. Despite this, there is a lack of a comprehensive review of the inherent inference mechanisms of deep learning for multi-modal sensor fusion. This work investigates up-to-date developments in multi-modal sensor fusion via deep learning to provide a broad picture of data fusion needs and technologies. It compares the characteristics of multi-modal data for various sensors, summarizes background concepts about data fusion and deep learning, and carefully reviews a large number of investigations in four inference mechanisms: adaptive learning, deep generative, deep discriminative, and algorithms unrolling. The pros and cons of the above methodologies are presented, and several popular application domains are discussed, including medical imaging, autonomous driving, remote sensing, and robotics. A large collection of multi-modal datasets published in recent years is presented, and several tables that quantitatively compare and summarize the performance of fusion algorithms are provided. Finally, by acknowledging the limitations of current research, we establish potential open challenges and future directions as guidance for deep learning-based multi-sensor fusion.</span><br>
</p>]]></description>
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        </item>
        <item>
            <title><![CDATA[Efficient Graph-based Signal Motif Discovery with Performance Bounds]]></title>
            <description><![CDATA[<p>
    <span><big>Efficient Graph-based Signal Motif Discovery with Performance Bounds</big></span><br>
</p>
<p>
    <span><small><i>Zeyu You, Raviv Raich, Yonghong Huang</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109178</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>In this paper, we investigate the problem of signal motif discovery. In the formulation considered, the goal is to find an unknown pattern (or motif) repeated across multiple data sets. This problem formulation can be applied to various domains, such as DNA sequence alignment and matching, motif discovery in time-series, and object detection and localization in images. We take the approach of minimizing an objective that is the sum over a measure of the difference between a candidate instance in each signal collection (or set) and the unknown pattern. Additionally, a non-negative instance dependent penalty is introduced. The proposed general objective can be used to capture well-known problems (e.g., blind joint time-delay estimation). Due to the non-convex nature of the problem and often the integer programming flavor of the approach, brute-force solution is non-polynomial and computationally prohibitive for large scale problems. We propose an efficient polynomial time (quadratic in the number of instances) bipartite graph based approximation to solve the problem. We provide a theoretical analysis for the proposed solution including bounds on the gap from the optimal solution and conditions for optimality. In particular, we show that the objective value for the proposed solution is no more than twice the objective value of the optimal solution. To illustrate the merit of the proposed approach, we present qualitative and quantitative empirical analysis of the proposed approach on several applications and compare our method to appropriate alternatives.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002529</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002529</link>
        </item>
        <item>
            <title><![CDATA[Local-and-Nonlocal Spectral Prior Regularized Tensor Recovery for Cauchy Noise Removal]]></title>
            <description><![CDATA[<p>
    <span><big>Local-and-Nonlocal Spectral Prior Regularized Tensor Recovery for Cauchy Noise Removal</big></span><br>
</p>
<p>
    <span><small><i>Yong-Ting Zhao, Wen-Fei Cao, Yang Chen</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109176</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>Cauchy noise removal in multidimension visual data is a fundamental problem in image processing and data science communities. Recently, this problem can be effectively solved by resorting to an appropriate tensor recovery approach and has attracted increasing attention. However, it is not extensively reported whether tensor recovery approaches with multiple priors can achieve even better denosing performance. To address this issue, we propose a novel tensor recovery approach in this paper based on the maximum a posteriori estimation (MAP). Our approach is a nonconvex optimization model that includes a composite regularization term and a data fidelity term. The composite regularization term incorporates nonlocal self-similarity in the spatial and gradient domains of multidimensional visual data, preserving both local image details and non-local structures. The data fidelity term is obtained by computing the negative log-likelihood of Cauchy noise distribution. To solve this proposed tensor recovery model, we develop an effective denoising algorithm with a global convergence guarantee via the well-known nonconvex alternating direction method of multipliers (ADMM). Finally, we conduct extensive numerical experiments to demonstrate that our proposed recovery approach can achieve superior denoising performance, both quantitatively and qualitatively, compared to existing common methods.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002505</link>
        </item>
        <item>
            <title><![CDATA[State space model-based harmonic decomposition of pseudo-periodic non-stationary multivariate signals]]></title>
            <description><![CDATA[<p>
    <span><big>State space model-based harmonic decomposition of pseudo-periodic non-stationary multivariate signals</big></span><br>
</p>
<p>
    <span><small><i>Luis Enrique Avendaño, Luis David Avendaño-Valencia, Edilson Delgado-Trejos, David Cuesta-Frau</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109192</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>This work is concerned with the harmonic decomposition of pseudo-periodic non-stationary multivariate signals. In this framework, a signal component corresponds to a single sinusoid with time-dependent amplitude and frequency. Thus, in the proposed harmonic decomposition, all the signal components are bound to share the same fundamental time-dependent frequency. Here, we establish a procedure to yield such decomposition by casting the multivariate signal into a block-diagonal state-space representation with multiple outputs. The harmonic components and instantaneous frequency are estimated using Kalman filtering. In addition, we provide an optimisation framework for the hyperparameters of the state space representation based on the Expectation-Maximisation algorithm. The proposed method provides an accurate, compact and physically explainable procedure for analysing pseudo-periodic non-stationary multiple-output systems, as demonstrated in the case of a simulated unbalanced rotor system and on real electrocardiographic records.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002669</link>
        </item>
        <item>
            <title><![CDATA[Learning from small data for hyperspectral image classification]]></title>
            <description><![CDATA[<p>
    <span><big>Learning from small data for hyperspectral image classification</big></span><br>
</p>
<p>
    <span><small><i>Xiaoyan Luo, Sen Li, Xiaofeng Shi, Jihao Yin</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109183</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>With the development of deep learning technique, many hyperspectral image classification (HIC) methods achieve great progress based on the application of convolution neural network (CNN). However, most of them still face small data cases. In this paper, we study the challenges of small data in HIC and propose a stronger HIC small data learning framework (SDLF). More specifically, a feature enhancement module named high-low dimension hybrid representation (HLDR) is designed to improve the feature differentiation power. To generate higher quality classification labels for unlabeled data, a smooth label generation module (SLG) is introduced based on Spatio-Temporal constraint. Then, we introduce a dynamically training sample amplification (TSA) under a unified curriculum learning strategy to add generated reliable labels into training samples. Finally, integrating HLDR in feature representation, SLG in label generation, and TSA in training amplification, the proposed SDLF framework is constructed, which can ensure correctness of HIC network. Experimental results demonstrate that our SDLF can not only learn robust feature representation to enhance the differentiation power but also select reliably generated labels to enrich the training data, which is useful for learning from small data. Also, our SDLF method achieves a superior performance with small labeled training data on three typical datasets.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002578</link>
        </item>
        <item>
            <title><![CDATA[Testing for Granger causality using a partial coherence statistic]]></title>
            <description><![CDATA[<p>
    <span><big>Testing for Granger causality using a partial coherence statistic</big></span><br>
</p>
<p>
    <span><small><i>Louis Scharf, Yuan Wang</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109190</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>In this paper, partial coherence is used to evaluate Granger-Geweke (GG) causality of one signal sequence on another. The key idea is to establish a connection between questions of GG causality and questions of partial coherence. Once this connection is established, then a scale-invariant partial coherence statistic is used to resolve the question of GG causality. This coherence statistic is shown to be a likelihood ratio, and its null distribution is shown to be a Wilks Lambda. Numerical experiments demonstrate the application of partial coherence to the resolution of Granger causal influence. Importantly, the method depends on no generative model for causality.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002645</link>
        </item>
        <item>
            <title><![CDATA[Adaptive sparse modeling in spectral & spatial domain for compressed image restoration]]></title>
            <description><![CDATA[<p>
    <span><big>Adaptive sparse modeling in spectral &amp; spatial domain for compressed image restoration</big></span><br>
</p>
<p>
    <span><small><i>Amit Soni Arya, Susanta Mukhopadhyay</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109191</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>Block discrete cosine transform (BDCT) is an indispensable component of modern image and video coding standards, specifically for its decorrelation and superior energy compaction aspects. BDCT typically employs block-specific quantization, which results in unpleasant compression-blocking artifacts which predominates at low bit rates. The proposed method aims to minimize these blocking artifacts to generate high-quality images under the framework of the alternating direction method of multipliers (ADMM) optimization. The proposed method exploits the local structures identified and extracted via wavelet-patch-based sparse representation and non-local self-similarity identified and extracted via group-based sparse representation, which are subsequently combined optimally employing ADMM. Moreover, the method uses a Gaussian quantization noise model, which allows a more precise and reliable assessment. An adaptive regularization parameter is used, which integrates spectral and spatial domain sparse representations with multi-resolution dictionaries and PCA-based dictionary. The proposed algorithm improves the overall practicality of the process and outperforms existing methods in terms of objective measures like structural similarity index measure, peak signal-to-noise ratio and visual perception.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002657</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002657</link>
        </item>
        <item>
            <title><![CDATA[Support recovery with Projected Stochastic Gates: Theory and application for linear models]]></title>
            <description><![CDATA[<p>
    <span><big>Support recovery with Projected Stochastic Gates: Theory and application for linear models</big></span><br>
</p>
<p>
    <span><small><i>Soham Jana, Henry Li, Yutaro Yamada, Ofir Lindenbaum</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109193</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise. We study the problem of estimating the model coefficients based on a recently proposed non-convex regularizer, namely the stochastic gates (STG) (Yamada et al., 2020). We suggest a new projection-based algorithm for solving the STG regularized minimization problem, and prove convergence and support recovery guarantees of the STG-estimator for a range of random and non-random design matrix setups. Our new algorithm has been shown to outperform the existing STG algorithm and other classical estimators for support recovery in various real and synthetic data analyses.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002670</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002670</link>
        </item>
        <item>
            <title><![CDATA[An end-to-end multiple side-outputs fusion deep supervision network based remote sensing image change detection algorithm]]></title>
            <description><![CDATA[<p>
    <span><big>An end-to-end multiple side-outputs fusion deep supervision network based remote sensing image change detection algorithm</big></span><br>
</p>
<p>
    <span><small><i>Xiaosuo Wu, Le Yang, Yaya Ma, Chaoyang Wu, ... Yufeng Fan</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109203</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>Change detection is an effective means of monitoring surface changes. In order to solve the problem of reducing detection accuracy in remote sensing image change detection methods that only focus on extracting deep semantic information while ignoring high-resolution shallow information, this paper proposes an end-to-end multi-side output fusion deep supervised network for high-resolution biphasic remote sensing image change detection. The enhanced feature extraction module is used to increase the Receptive field and capture multi-scale features. The dense skip connection module utilizes skip connections to fuse shallow position information and deep semantic information, alleviating the problem of feature information loss. The deep supervision module with multiple side-outputs fusion utilizes the information complementarity between different scale feature maps, combined with multi-scale prediction maps, to improve the robustness of target scale changes. The combined Loss function is used to alleviate the problem of imbalance between positive and negative samples, so that more attention is paid to the learning of changing characteristics in the process of training the network. The proposed method was validated on CDD dataset and DSIFN dataset, with F1 scores of 91.58% and 86.69%, respectively, which were improved by 1.00% and 5.53% compared to the suboptimal network.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002773</link>
        </item>
        <item>
            <title><![CDATA[Adaptive LiDAR Reconstruction by Convolutional Compressive Sensing Network and Multivariate Empirical Mode Decomposition]]></title>
            <description><![CDATA[<p>
    <span><big>Adaptive LiDAR Reconstruction by Convolutional Compressive Sensing Network and Multivariate Empirical Mode Decomposition</big></span><br>
</p>
<p>
    <span><small><i>Rajat C. Shinde, Surya S Durbha</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109194</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>With the recent advancement of LiDAR data processing pipelines integrated with signal processing techniques, it is now possible to rapidly acquire, process, and infer multidimensional information of 3D surroundings. This work proposes a novel compressive sensing (CS) framework (named MEMD-CSNet), based on Multivariate Empirical Mode Decomposition (MEMD) for adaptive 3D reconstruction of airborne urban LiDAR point clouds. EMD is a data-driven transform for decomposing the input data into Intrinsic Mode Functions (IMFs), capturing the implicit features in terms of variation in spatial frequency. Our approach generates a new filtered 3D signal constituting an optimal subset of IMFs based on the understanding of their statistical significance. Further, this filtered signal hierarchically extracts the overall shape in the last IMF (Residual function). Our proposed MEMD-CSNet is a deep convolutional network that (1) computes a multivariate EMD on input raw 3D LiDAR point clouds for generating data-dependent feature-rich filtered signal and (2) additionally augments this signal with convolutional features, thus learning a data-centric representation function (Ψ) for transforming the input into a low-dimensional sparse basis. We present extensive qualitative and quantitative analysis using the proposed MEMD-CSNet for a set of chosen CS measurement ratios. MEMD-CSNet achieves a maximum reconstruction PSNR of 108.22&nbsp;dB (approx. 30&nbsp;dB/1.36x better than without using MEMD) for reconstruction fidelity on selected urban LiDAR scenes. We envisage that our proposed MEMD-CSNet has a high potential (as a data-centric framework) for generating data-driven sparse feature maps and reconstructing entire 3D LiDAR point clouds from a few sparse measurements.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002682</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002682</link>
        </item>
        <item>
            <title><![CDATA[Improved analytical learning proximal operator method for sparse recovery]]></title>
            <description><![CDATA[<p>
    <span><big>Improved analytical learning proximal operator method for sparse recovery</big></span><br>
</p>
<p>
    <span><small><i>Tao Pu, Weike Feng, Ningning Tong, Pengcheng Wan, Xiaowei Hu</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109198</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>Sparse recovery (SR) has received considerable attention in the last decades. Various iterative algorithms have been proposed to solve the SR problem, but most of them are unsatisfactory in terms of convergence accuracy and speed. Recently, deep unfolding methods achieve a dramatic improvement in convergence by unfolding iterative SR algorithms into deep neural networks and learning their parameters from training data, e.g., Learning Proximal Operator Method (LePOM) and Analytical LePOM (ALePOM). However, according to theoretical analysis, the recovery error bound of ALePOM should be further reduced as its network parameters are learned to fit all training data. To solve this problem, we design a new network named Improved ALePOM (I-ALePOM) by introducing a Long Short-Term Memory (LSTM) cell into each layer of ALePOM, which helps to adaptively compute the thresholds and step-sizes for each training data. By doing so, the recovery error bound becomes tighter and higher sparse recovery performance can be achieved. Furthermore, we extend the proposed I-ALePOM from the 1-D vector form to the 2-D matrix form, reducing the memory and computation burdens for practical sparse matrix recovery applications. Various numerical simulations verify that the proposed method has a higher performance than the state-of-the-art methods in the literature.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002724</link>
        </item>
        <item>
            <title><![CDATA[The design of distributed filtering based on lattice rule]]></title>
            <description><![CDATA[<p>
    <span><big>The design of distributed filtering based on lattice rule</big></span><br>
</p>
<p>
    <span><small><i>Shihang Li, Zhiheng Zhang, Peng Liu, Jianfeng Cui</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109185</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>For the problem of multiple micro-target tracking and positioning, the high-dynamic and communication coupling are two cases in actual motion. The dynamic evolution and measurement among them are depicted by a nonlinear system. This paper proposes a new distributed lattice Kalman filter (DLKF) to cope with the two cases simultaneously. The prediction is based on the Cranley-Patterson shift and Korobov lattice rule to generate the low-difference sample points. The update stage is based on the weighted average consistency to fusion its innovation and the information of its neighbor. The stability of this distributed filter is proved by constructing the upper and lower bound of the Lyapunov functional. The finite steps consensus iteration strategies for the update fusion are also investigated. To evaluate the efficiency of the DLKF, it is compared with unscented Kalman filtering and its distributed form. The simulation results show that DLKF uses significantly fewer sampling points than other quasi-Monte Carlo (QMC) filters while maintaining the estimation accuracy. The computational complexity is significantly lower, which is the key factor for application to multiple micro-targets.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002591</link>
        </item>
        <item>
            <title><![CDATA[Steady-state mean-square performance analysis of the block-sparse maximum Versoria criterion]]></title>
            <description><![CDATA[<p>
    <span><big>Steady-state mean-square performance analysis of the block-sparse maximum Versoria criterion</big></span><br>
</p>
<p>
    <span><small><i>Ben-Xue Su, Fei-Yun Wu, Kun-De Yang, Tian Tian, Yi-Yang Ni</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109186</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>The maximum Versoria criterion algorithm (MVC) exhibits lower steady-state misalignment and less complexity as compared to the maximum correntropy criterion (MCC) algorithm in the scenario of non-Gaussian impulsive noises. However, few scholars have discussed improving the MVC algorithm in sparse channels. This paper presents a block-sparse MVC (BS-MVC) algorithm by introducing the regularization norm, which has desirable performance in block-sparse channels and has excellent robustness to non-Gaussian conditions with impulsive noises. The steady-state excess mean-square error (EMSE) is discussed in Gaussian and non-Gaussian noise conditions. The effectiveness of BS-MVC and the theoretical expression is validated using multiple simulations. The BS-MVC achieves a lower steady-state mean-square deviation (MSD) and maintains a fast convergence rate compared with MCC and MVC algorithms.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002608</link>
        </item>
        <item>
            <title><![CDATA[Improved CNN-based CatBoost model for license plate remote sensing image classification]]></title>
            <description><![CDATA[<p>
    <span><big>Improved CNN-based CatBoost model for license plate remote sensing image classification</big></span><br>
</p>
<p>
    <span><small><i>Songhua Zhang, Xiuling Lu, Zhangjie Lu</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109196</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>The rapid development of artificial intelligence technology has brought profound technical changes to many traditional industries, among which intelligent transportation has become a hot spot for development in the traditional transportation field. Remote sensing license plate image recognition technology is extensively emphasized in such domains as intelligent transportation and intelligent vehicle management. However, in the practical traffic environment, low visibility scenes caused by complex environmental factors such as rain, snow, haze and cloudy days influence the recognition and classification of license plates, while the distortion of license plate images that may be caused by irregular movements of vehicles bring challenges to license plate recognition classification. The CNN-CatBoost model proposed in this paper divides the license plate recognition classification into two stages. The first stage uses the excellent performance of convolutional neural network in processing image data to extract various license plate image features; the second stage uses the CatBoost module to further process the image feature data and finally obtain the remote sensing license plate information. The model achieves outstanding results in the experiments and has practical application value. Through comparison with other network models, the CNN-CatBoost model proposed in this paper has superior performance.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002700</link>
        </item>
        <item>
            <title><![CDATA[The effect of source sparsity on independent vector analysis for blind source separation]]></title>
            <description><![CDATA[<p>
    <span><big>The effect of source sparsity on independent vector analysis for blind source separation</big></span><br>
</p>
<p>
    <span><small><i>Jianjun Gu, Longbiao Cheng, Dingding Yao, Junfeng Li, Yonghong Yan</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109199</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>In this paper, the effect of source sparsity on the performance of the independent vector analysis (IVA) algorithm for blind source separation is investigated. The IVA algorithm was originally developed under the assumption of statistical independence between the sources and has made great advances in recent years. However, its performance under different sparsity conditions is rarely studied. This study begins by mathematically analyzing the performance of IVA in permutation alignment, which is proved to directly correlate with the degree of frame-level W-disjoint orthogonality (F-WDO) of the sources. We further prove that IVA can theoretically achieve the optimal separation in the cases where the sources are F-WDO. Experimental results show a strong positive correlation between a quantitative measure of F-WDO and the IVA algorithm’s performance under various conditions.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002736</link>
        </item>
        <item>
            <title><![CDATA[System error estimation for sensor network with integrated sensing and communication application]]></title>
            <description><![CDATA[<p>
    <span><big>System error estimation for sensor network with integrated sensing and communication application</big></span><br>
</p>
<p>
    <span><small><i>Junkun Yan, Ruiyang Zhai, Tihua Yan, Wenqiang Pu, ... Hongwei Liu</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109200</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>The integrated sensing and communication base station can perform both communication and sensing tasks simultaneously. The key premise of accurate localization and tracking of targets is sensor registration process. If system errors of multi-sensor system are not corrected properly, it can lead to significant sensing errors and reduce the overall system performance. In this paper, we propose a system error estimation method for a 3-dimensional asynchronous base station network to achieve satisfactory localization and tracking performance. Specifically, by making use of the priori information of the cooperative targets, we propose to minimize the sum of squares of the position mismatch errors of the targets, this leads to a nonlinear error estimation model for the system errors. To handle the high nonlinearity and ensure fast convergence of online registration, an efficient inexact block coordinate descent optimization algorithm incorporating the proximal method is proposed. The algorithm divides the estimation problem into a linear least square subproblem and several quadratic constrained quadratic program subproblems, enabling different kinds of system errors to be alternatively updated. Finally, the effectiveness and feasibility of the proposed estimation algorithm are demonstrated through numerical simulation.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002748</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002748</link>
        </item>
        <item>
            <title><![CDATA[Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation]]></title>
            <description><![CDATA[<p>
    <span><big>Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation</big></span><br>
</p>
<p>
    <span><small><i>Prabhu Babu, Petre Stoica</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109189</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>We consider the problem of model selection for high-dimensional sparse linear regression models. We pose the model selection problem as a multiple-hypothesis testing problem and employ the methods of false discovery rate (FDR) and familywise error rate (FER) to solve it. We also present the reformulation of the FDR/FER-based approaches as criterion-based model selection rules and establish their relation to the extended Bayesian Information Criterion (EBIC), which is a state-of-the-art high-dimensional model selection rule. We use numerical simulations to show that the proposed FDR/FER method is well suited for high-dimensional model selection and performs better than EBIC.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S0165168423002633</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002633</link>
        </item>
        <item>
            <title><![CDATA[An incremental noise constrained LMS algorithm]]></title>
            <description><![CDATA[<p>
    <span><big>An incremental noise constrained LMS algorithm</big></span><br>
</p>
<p>
    <span><small><i>Muhammad Omer Bin Saeed, Azzedine Zerguine, Usman Hameed, Sajid Gul Khawaja, Oualid Hammi</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109187</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>While a lot of research has focused on diffusion-based algorithms for distributed estimation over adaptive networks, incremental algorithms have not been studied to that extent. Here we present an incremental scheme-based distributed least mean square algorithm that uses the variance of the additive noise as a constraint. The proposed algorithm is derived and then its theoretical performance analysis is presented. Simulation results for different scenarios are then presented and it is shown that the simulation results corroborate very well the theoretical findings. More importantly, our algorithm outperforms, a recently proposed algorithm, the variable step-size incremental-based LMS (VSSILMS) algorithm.</span><br>
</p>]]></description>
            <guid isPermaLink="false">https://www.sciencedirect.com/science/article/pii/S016516842300261X</guid>
            <link>https://www.sciencedirect.com/science/article/pii/S016516842300261X</link>
        </item>
        <item>
            <title><![CDATA[Centralized multi-sensor labeled multi-Bernoulli filter with partially overlapping fields of view]]></title>
            <description><![CDATA[<p>
    <span><big>Centralized multi-sensor labeled multi-Bernoulli filter with partially overlapping fields of view</big></span><br>
</p>
<p>
    <span><small><i>Bailu Wang, Yuhang Xu, Suqi Li, Giorgio Battistelli, Luigi Chisci</i></small></span><br>
    <span><small><i>https://doi.org/10.1016/j.sigpro.2023.109180</i></small></span><br>
    <span><small><i>Volume 213</i></small></span><br>
</p>
<p>
    <span>In this paper, the labeled multi-Bernoulli (LMB) filter is extended to the centralized multi-sensor (MS) case, resulting in a multi-sensor multi-object tracking (MS-MOT) algorithm having the ability to cope with partially overlapping fields-of-view (FoVs). Specifically, a joint prediction and update scheme for the MS-LMB filter is presented and the inherent NP-hard multi-dimensional assignment problem is solved via Gibbs sampling. We propose a suboptimal stationary distribution for the Gibbs sampling which admits conditionally independent sampling among multiple sensors thus dramatically decreasing the computational complexity. In addition, in order to tune object initialization when there is no background knowledge of the birth locations, a novel MS measurement-driven birth process is proposed. The proposed birth process has superior ability in terms of suppressing false tracks originating from clutter measurements of multiple sensors. The effectiveness of the proposed algorithm and birth process is demonstrated in simulation experiments concerning MS surveillance scenarios with partially overlapping sensor FoVs.</span><br>
</p>]]></description>
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            <link>https://www.sciencedirect.com/science/article/pii/S0165168423002542</link>
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