Signal Processing最新文献

{"title":"Incomplete multi-view semi-supervised classification via dual-graph structure and dual-contrastive completion","authors":"Xinchao Lu ,&nbsp;Lihua Zhou ,&nbsp;Ting Zhang ,&nbsp;Lizhen Wang","doi":"10.1016/j.sigpro.2025.109942","DOIUrl":"10.1016/j.sigpro.2025.109942","url":null,"abstract":"<div><div>In the real world, data often have multiple views, and learning from these multi-view data can improve the accuracy and robustness of classification models. The success of existing multi-view classification relies on a large amount of labeled and complete multi-view data. However, this is very difficult for practical applications due to data collection techniques failures and expensive labeling costs. To address this challenge, this paper proposes a new incomplete multi-view semi-supervised classification framework. Specifically, we first consider the potential graph structures among samples from specific views and a global view separately, aiming to extract specific information from each view and integrate complementary information across views. Then, we designed a completion module based on view-specific graphs and dual-contrastive learning. This module completes missing views of samples based on the spatial similarity relationships among samples in view-specific graphs, and then enhances the discriminative ability of the completion using dual contrastive learning. Finally, a classifier based on a global-specific graph and graph convolutional network (GCN) is designed to classify unlabeled samples by using spatial relationships among all samples and scarce labels. Extensive experiments, including comparison with existing algorithms, visualization analysis, ablation experiments, and parameter sensitivity examination, are conducted on seven real world datasets to showcase the effectiveness of the proposed framework.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109942"},"PeriodicalIF":3.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420051","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":"Low-rank tensor completion via tensor tri-factorization and sparse transformation","authors":"Fanyin Yang ,&nbsp;Bing Zheng ,&nbsp;Ruijuan Zhao","doi":"10.1016/j.sigpro.2025.109935","DOIUrl":"10.1016/j.sigpro.2025.109935","url":null,"abstract":"<div><div>Low-rank tensor factorization techniques have gained significant attention in low-rank tensor completion (LRTC) tasks due to their ability to reduce computational costs while maintaining the tensor’s low-rank structure. However, existing methods often overlook the significance of tensor singular values and the sparsity of the tensor’s third-mode fibers in the transformation domain, leading to an incomplete capture of both the low-rank structure and the inherent sparsity, which limits recovery accuracy. To address these issues, we propose a novel tensor tri-factorization logarithmic norm (TTF-LN) that more effectively captures the low-rank structure by emphasizing the significance of tensor singular values. Building on this, we introduce the tensor tri-factorization with sparse transformation (TTF-ST) model for LRTC, which integrates both low-rank and sparse priors to improve accuracy of incomplete tensor recovery. The TTF-ST model incorporates a sparse transformation that represents the tensor as the product of a low-dimensional sparse representation tensor and a compact orthogonal matrix, which extracts sparsity while reducing computational complexity. To solve the proposed model, we design an optimization algorithm based on the alternating direction method of multipliers (ADMM) and provide a rigorous theoretical analysis. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in both recovery accuracy and computational efficiency.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109935"},"PeriodicalIF":3.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429936","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":"Direction of arrival estimation for sparse arrays with gain-phase errors in nonuniform noise environment","authors":"Yule Zhang ,&nbsp;Hao Zhou ,&nbsp;Junpeng Shi ,&nbsp;Guimei Zheng ,&nbsp;Guoping Hu ,&nbsp;Yuwei Song ,&nbsp;Fei Zhang","doi":"10.1016/j.sigpro.2025.109940","DOIUrl":"10.1016/j.sigpro.2025.109940","url":null,"abstract":"<div><div>The emerging sparse arrays achieve enhanced direction of arrival (DOA) estimation by flexibly deploying sensors and fully extracting the structural information contained in the incident sources. However, the existing DOA estimation algorithms for sparse arrays typically yield satisfactory performance only in ideal or single non-ideal scenarios. In this work, we address the issue of DOA estimation for sparse arrays under the coexistence of gain-phase errors and nonuniform noise. The analysis of the negative impact of these two types of non-idealities on virtual array processing motivates us to develop new algorithm. Specifically, with the perturbation of gain-phase errors, a least squares optimization program is first constructed to solve the nonuniform noise power. Then, based on the initial gain errors obtained by exploiting the diagonal entries in the denoised covariance matrix, we implement the iterative estimation of DOAs and gain-phase errors with the aid of the eigenstructure-based subspace approach. To improve the DOA estimation accuracy, we formulate the difference coarray interpolation problem and introduce the truncated nuclear norm minimization to recover the missing information. The developed algorithm can overcome the effects of gain-phase errors and nonuniform noise simultaneously. Numerical simulations demonstrate that the developed algorithm outperforms its competitors.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109940"},"PeriodicalIF":3.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471134","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":"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}