Signal Processing最新文献

{"title":"Deep fully-connected tensor network decomposition for multi-dimensional signal recovery","authors":"Ruoyang Su ,&nbsp;Xi-Le Zhao ,&nbsp;Wei-Hao Wu ,&nbsp;Sheng Liu ,&nbsp;Junhua He","doi":"10.1016/j.sigpro.2025.109903","DOIUrl":"10.1016/j.sigpro.2025.109903","url":null,"abstract":"<div><div>Fully-connected tensor network (FCTN) decomposition has garnered significant interest for processing multi-dimensional signals due to its ability to capture the all-mode correlations of a tensor. However, its representation ability is still limited, particularly for representing fine details and complex textures. To break this limitation, we propose deep fully-connected tensor network (D-FCTN) decomposition with a powerful representation ability beyond FCTN decomposition. Specifically, D-FCTN decomposition consists of two pivotal building blocks: the intrinsic low-rank representation block and the deep transform block. In the intrinsic low-rank representation block, we use FCTN decomposition to capture the all-mode correlations in the low-dimensional latent space, which implicitly regularizes the recovered signal. In the deep transform block, the latent space is transformed to the original signal space by leveraging a deep neural network due to its mighty expressive capability. The intrinsic low-rank representation boosted by the deep transform is expected to deliver a more powerful representation ability for recovering multi-dimensional signals beyond FCTN decomposition. To examine the representation ability of D-FCTN decomposition, we suggest an unsupervised D-FCTN decomposition-based multi-dimensional signal recovery model. Experiments on multi-dimensional signals demonstrate the more powerful representation ability of D-FCTN decomposition especially for recovering fine details and complex textures, compared with the state-of-the-art methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109903"},"PeriodicalIF":3.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFLM: A time-frequency domain learning model for underwater acoustic signal reconstruction","authors":"Ming Xu ,&nbsp;Linjiang Liu","doi":"10.1016/j.sigpro.2025.109936","DOIUrl":"10.1016/j.sigpro.2025.109936","url":null,"abstract":"<div><div>The reconstruction of underwater acoustic signals affected by seasonal variations is of great significance to improve the accuracy and stability of oceanic communication systems. Seasonal changes in water temperature, salinity, and density cause severe fluctuations and distortions to the underwater acoustic signals, which degrade the signal transmission quality. Existing reconstruction methods are often inadequate in adapting to these complex and dynamic environments and remain susceptible to noise interference. To tackle these challenges, we propose a novel Time-Frequency domain Learning Model (TFLM) for underwater acoustic signal reconstruction. TFLM decomposes the distorted signal into trend and seasonal components for reconstruction. The trend components are reconstructed using an enhanced Long Short-Term Memory (En- LSTM) that effectively captures long-term temporal features. For the seasonal components, a multi-layer encoder–decoder architecture is utilized to extract local features and address seasonal fluctuations. Extensive experimental evaluations demonstrate that TFLM outperforms existing methods in terms of reconstruction accuracy, providing a robust solution for underwater acoustic signal reconstruction under seasonal variability.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109936"},"PeriodicalIF":3.4,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D mmW sparse imaging via complex-valued composite penalty function within collaborative multitasking framework","authors":"Yangyang Wang ,&nbsp;Liming Zhou ,&nbsp;Xu Zhan ,&nbsp;Guohao Sun ,&nbsp;Yuxuan Liu","doi":"10.1016/j.sigpro.2025.109939","DOIUrl":"10.1016/j.sigpro.2025.109939","url":null,"abstract":"<div><div>The emerging three-dimensional (3D) millimeter-wave (mmW) array SAR imaging with compressed sensing (CS) has shown impressive potential for improving image quality. However, the widely used <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> penalty function belongs to convex operators, which introduce bias effects in imaging and reduce reconstruction accuracy. In the context of 3D imaging, a single <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> is inadequate for characterizing the spatial features of the target, resulting in the loss of information. Additionally, the complex-valued nature of SAR data should be considered to further improve imaging performance. Therefore, in this article, a 3D sparse imaging method based on the complex-valued composite penalty function (CCPF) is proposed. Firstly, a CCPF is presented, which combines complex-valued minimax convex penalty (CMCP) and complex-valued 3D total variation (C3DTV) to alleviate bias effects while preserving the spatial structure information of the target. Secondly, the improved collaborative multitasking framework based on variable splitting and alternating minimization is presented to solve optimization problems with CCPF. Furthermore, the proposed method takes into account the complex-valued characteristics of SAR data and preserves the phase information of the imaging scene, which is beneficial for subsequent image interpretation. Finally, the effectiveness of the proposed method has been validated by a substantial amount of experimental data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109939"},"PeriodicalIF":3.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of unconstrained ℓp minimization for sparse recovery","authors":"Jianwen Huang ,&nbsp;Xinling Liu ,&nbsp;Feng Zhang ,&nbsp;Guowang Luo ,&nbsp;Runbin Tang","doi":"10.1016/j.sigpro.2025.109937","DOIUrl":"10.1016/j.sigpro.2025.109937","url":null,"abstract":"<div><div>In view of coherence, this paper firstly presents a coherence-based theoretical guarantee, including a sufficient condition and associated error estimate, for a non-convex unconstrained <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> (<span><math><mrow><mn>0</mn><mo>&lt;</mo><mi>p</mi><mo>≤</mo><mn>1</mn></mrow></math></span>) minimization to robustly reconstruct any non-sparse signal in the noisy situation. In a sense, this result supplements the preceding founded ones for the constrained <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> minimization. Specially, when <span><math><mrow><mi>p</mi><mo>=</mo><mn>1</mn></mrow></math></span>, our coherence-based condition reduces to the state-of-art sharp one, i.e, <span><math><mrow><mi>μ</mi><mo>&lt;</mo><mn>1</mn><mo>/</mo><mrow><mo>(</mo><mn>2</mn><mi>s</mi><mo>−</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>. It should also be emphasized that for sparse metric models, based on the theory of coherence, our condition has reached consistency with the corresponding constraint situation. According to the established result, the error in the case that the representative constrained <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> minimization is substituted with unconstrained <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> minimization is also studied. Additionally, the relationship between coherence and null space property (NSP) is discussed and the derived result claims that coherence could imply NSP. In view of the induced NSP, the recovery theory that guarantees the sparse signal can be recovered via the unconstrained <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> minimization is established. Based on the synthetic signals and the real-life signals, it is demonstrated by experimental results that compared with state-of-art methods and convex <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> minimization, the performance of non-convex <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> minimization is more competitive.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109937"},"PeriodicalIF":3.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and implementation of an adaptive extended Kalman filter with interval type-3 fuzzy set for an attitude and heading reference system","authors":"Javad Faraji ,&nbsp;Jafar Keighobadi ,&nbsp;Farrokh Janabi-Sharifi","doi":"10.1016/j.sigpro.2025.109947","DOIUrl":"10.1016/j.sigpro.2025.109947","url":null,"abstract":"<div><div>There are various techniques for estimating dynamic systems. Two of the most commonly used filters are the linear Kalman filter and the extended Kalman filter. However, these methods have some limitations since both the dynamic equations and the measurement equations need to be linearized. In addition, uncertainties in the dynamic equations and noise in the measurement equations must be considered when implementing these algorithms, which can be seen in the accurate determination of the covariance matrix of the process and measurement noise. This study focuses on improving the accuracy of the attitude and heading reference system through improved extended Kalman filter performance. This improvement is achieved by adjusting the covariance matrix with respect to the measurement noise using the interval type-3 fuzzy set in conjunction with the covariance matching technique. The novel algorithm is tested on a ground vehicle in an urban environment and its effectiveness compared to the traditional extended Kalman filter is demonstrated.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109947"},"PeriodicalIF":3.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel block sparse Bayesian learning for high dimensional sparse signals","authors":"Oisín Boyle ,&nbsp;Murat Üney ,&nbsp;Xinping Yi ,&nbsp;Joseph Brindley","doi":"10.1016/j.sigpro.2025.109938","DOIUrl":"10.1016/j.sigpro.2025.109938","url":null,"abstract":"<div><div>We address the recovery of block sparse signals by proposing a distributed solution that uses a block-diagonal approximation to the dictionary matrix of the problem. The approximation is found in two stages. First, the Gram matrix of the dictionary matrix is used as a basis for spectral clustering. Afterwards, measurement positions are assigned to the clusters formed from this spectral clustering. The method is then applied to use previous algorithms in the literature of Block Sparse Bayesian Learning in parallel. Moreover, this method also speeds up the algorithm in serial systems. The efficacy of the proposed method is demonstrated in simulations with comparison to the previous Block Sparse Bayesian Learning algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109938"},"PeriodicalIF":3.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfigurable intelligent surface-enabled gridless DoA estimation system for NLoS scenarios","authors":"Jiawen Yuan ,&nbsp;Gong Zhang ,&nbsp;Kaitao Meng ,&nbsp;Henry Leung","doi":"10.1016/j.sigpro.2025.109934","DOIUrl":"10.1016/j.sigpro.2025.109934","url":null,"abstract":"<div><div>The conventional direction-of-arrival (DoA) estimation approaches are effective only when the line-of-sight (LoS) link is available. In non-line-of-sight (NLoS) scenarios, it is challenging to effectively obtain the directional information of targets due to the uncontrollability of signal reflections from NLoS links. To handle this issue, a novel reconfigurable intelligent surface (RIS)-enabled gridless DoA estimation system for NLoS scenarios is proposed, where the RIS establishes a virtual LoS link between the base station and targets. First, considering the minable statistics of the signal, the RIS-enabled signal model in the covariance domain with a limited number of receiving antennas is proposed to help reduce resource consumption. Next, we estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to enhance the robustness to noise. Then, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem on the covariance-noiseless matrix. To reduce the computation, an efficient iterative approach is designed via the alternating direction method of multipliers. Furthermore, this system’s Cramér–Rao lower bound is derived, which is further exploited as the DoA estimation’s reference bound. Numerical experiments validate the superiority of the proposed system over the benchmark in terms of computational efficiency and estimation precision.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109934"},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topological signal processing and learning: Recent advances and future challenges","authors":"Elvin Isufi ,&nbsp;Geert Leus ,&nbsp;Baltasar Beferull-Lozano ,&nbsp;Sergio Barbarossa ,&nbsp;Paolo Di Lorenzo","doi":"10.1016/j.sigpro.2025.109930","DOIUrl":"10.1016/j.sigpro.2025.109930","url":null,"abstract":"<div><div>Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to algebraic tool for modeling the structure via nodes and edges capturing their interactions, leading to the establishment of the fields of graph signal processing (GSP) and graph machine learning (GML). Key graph-aware methods include Fourier transform, filtering, sampling, as well as topology identification and spatiotemporal processing. Although versatile, graphs can model only pairwise dependencies in the data. To this end, topological structures such as simplicial and cell complexes have emerged as algebraic representations for more intricate structure modeling in data-driven systems, fueling the rapid development of novel topological-based processing and learning methods. This paper first presents the core principles of topological signal processing through the Hodge theory, a framework instrumental in propelling the field forward thanks to principled connections with GSP-GML. It then outlines advances in topological signal representation, filtering, and sampling, as well as inferring topological structures from data, processing spatiotemporal topological signals, and connections with topological machine learning. The impact of topological signal processing and learning is finally highlighted in applications dealing with flow data over networks, geometric processing, statistical ranking, biology, and semantic communication.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109930"},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully-connected tensor network decomposition with gradient factors regularization for robust tensor completion","authors":"Bin Xiao,&nbsp;Heng-Chao Li,&nbsp;Rui Wang,&nbsp;Yu-Bang Zheng","doi":"10.1016/j.sigpro.2025.109933","DOIUrl":"10.1016/j.sigpro.2025.109933","url":null,"abstract":"<div><div>The robust tensor completion (RTC) problem focuses on recovering both a low-rank and a sparse component from noisy and incomplete observational data. The fully-connected tensor network (FCTN) decomposition has demonstrated remarkable effectiveness in capturing the global low-rank structure in high-dimensional data. However, prior research utilizing FCTN decomposition has predominantly considered global data correlations, which may lead to suboptimal recovery by ignoring local continuity. In this study, we present a model leveraging factor-regularized FCTN decomposition to tackle the RTC problem. Specifically, the global low-rank property is captured via FCTN decomposition, while the local continuity is enforced through constraints on the FCTN factors. Furthermore, to solve the proposed model, we develop a proximal alternating minimization (PAM) algorithm and prove its convergence theoretically. Finally, the effectiveness of the proposed method is validated through numerical experiments conducted on both color and hyperspectral video data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109933"},"PeriodicalIF":3.4,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust quaternion Kalman filter for state saturation systems with stochastic nonlinear disturbances","authors":"Dongyuan Lin ,&nbsp;Xiaofeng Chen ,&nbsp;Peng Cai ,&nbsp;Yunfei Zheng ,&nbsp;Qiangqiang Zhang ,&nbsp;Junhui Qian ,&nbsp;Shiyuan Wang","doi":"10.1016/j.sigpro.2025.109931","DOIUrl":"10.1016/j.sigpro.2025.109931","url":null,"abstract":"<div><div>To tackle the quaternion robust state estimation problem, the robust quaternion Kalman filter (RQKF) has been developed for quaternion signals by the quaternion maximum correntropy criterion (QMCC) under non-Gaussian noises. However, in the presence of saturation phenomena and nonlinear disturbances impacting quaternion systems, the performance of RQKF may deteriorate. Hence, this paper focuses on the quaternion Kalman filtering issue for state saturation systems with stochastic nonlinear disturbances under non-Gaussian noises. First, a feasible upper bound on the filtering error covariance is first obtained by some quaternion matrix techniques, and then a QMCC-based RQKF for state saturation systems (MCQKF-SS) is developed. The posterior estimate of the MCQKF-SS algorithm, developed as an iterative online method with a recursive structure, is updated by a quaternion iterative equation (QIE). Subsequently, a sufficient condition is proposed to ensure the uniqueness of the QIE’s fixed point, thereby guaranteeing the convergence of MCQKF-SS. Moreover, an adaptive kernel width strategy addresses the kernel width selection problem, leading to the development of a variable kernel width version of MCQKF-SS (VKMCQKF-SS). Finally, simulation results of two numerical examples verify the effectiveness and robustness of proposed quaternion algorithms in the considered environment.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109931"},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}