Signal Processing最新文献

{"title":"Cubic NK-SVD: An algorithm for designing parametric dictionary in frequency estimation","authors":"Xiaozhi Liu,&nbsp;Yong Xia","doi":"10.1016/j.sigpro.2025.110029","DOIUrl":"10.1016/j.sigpro.2025.110029","url":null,"abstract":"<div><div>We propose a novel parametric dictionary learning algorithm for line spectral estimation, applicable in both single measurement vector (SMV) and multiple measurement vectors (MMV) scenarios. This algorithm, termed cubic Newtonized K-SVD (NK-SVD), extends the traditional K-SVD method by incorporating cubic regularization into Newton refinements. The proposed Gauss–Seidel scheme not only enhances the accuracy of frequency estimation over the continuum but also achieves better convergence by incorporating higher-order derivative information. A key contribution of this work is the rigorous convergence analysis of the proposed algorithm within the Block Coordinate Descent (BCD) framework. To the best of our knowledge, this is the first convergence analysis of BCD with a higher-order regularization scheme. Moreover, the convergence framework we develop is generalizable, providing a foundation for designing alternating minimization algorithms with higher-order regularization techniques. Extensive simulations demonstrate that cubic NK-SVD outperforms state-of-the-art methods in both SMV and MMV settings, particularly excelling in the challenging task of recovering closely-spaced frequencies. The code for our method is available at <span><span>https://github.com/xzliu-opt/Cubic-NK-SVD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110029"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817740","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":"Double-optimized symmetric geometric mode decomposition with dispersion entropy and its application in feature extraction","authors":"Yuxing Li ,&nbsp;Xuanming Cheng","doi":"10.1016/j.sigpro.2025.110046","DOIUrl":"10.1016/j.sigpro.2025.110046","url":null,"abstract":"<div><div>Symmetric geometric mode decomposition (SGMD) offers notable advantages in preserving the basic features of time series and in noise robustness. However, SGMD faces issues related to inaccurate mode decomposition and parameter selection. To address these problems, this paper proposes a double-optimized symmetric geometric mode decomposition with dispersion entropy (DSGMDDE). This algorithm incorporates dispersion entropy(DisE) as an indicator for mode reconstruction, enhancing the accuracy of mode decomposition. Furthermore, a double optimization algorithm is introduced to optimize parameters, thereby improving the effectiveness of the algorithm. By combining DSGMDDE with DisE, a feature extraction method named DSGMDDE-DisE is proposed. Simulation results demonstrate that, compared to four other mode decomposition algorithms, DSGMDDE offers higher decomposition accuracy and better robustness. Furthermore, DSGMDDE-DisE shows superior feature extraction capability compared to the other four feature extraction methods. Real-world experiment results indicate that DSGMDDE-DisE can more accurately distinguish between eight types of ship radiated noises (SRNs) and five types of Southeast University bearings (SUBs) fault signals.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110046"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829834","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":"Irregular time-varying series prediction on graphs with nonlinear expansion functions","authors":"Wenjuan Li ,&nbsp;Ming Jin ,&nbsp;Junzheng Jiang ,&nbsp;Qinghua Guo ,&nbsp;Wanyuan Cai","doi":"10.1016/j.sigpro.2025.110025","DOIUrl":"10.1016/j.sigpro.2025.110025","url":null,"abstract":"<div><div>Predicting irregular time-varying series is challenging due to the complex interdependencies among variables. To capture the nonlinear spatiotemporal relationships in the data evolution process, we propose two nonlinear prediction methods that incorporate nonlinear expansion functions and graph signal processing (GSP). First, we develop a nonlinear graph vector autoregressive (NL-GVAR) model equipped with a nonlinear expansion module. This model maps graph signals from low-dimensional to high-dimensional spaces to enhance the nonlinear representation capability. Second, to address the impact of fluctuations in non-stationary time series, we integrate empirical mode decomposition (EMD) into the NL-GVAR framework. This integration allows for the efficient capture of the underlying nonlinear interdependencies within the time series. Furthermore, we derive closed-form solutions for parameter optimization under the minimum mean square error (MSE) criterion. Numerical results using various synthetic and real-world datasets demonstrate the superior performance of the proposed methods compared to existing methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110025"},"PeriodicalIF":3.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808788","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":"Sparse representation for ℓp−αℓq minimization and uniform condition for the recovery of approximately k-sparse signals with prior support information","authors":"Kunsheng Zhan,&nbsp;Anhua Wan","doi":"10.1016/j.sigpro.2025.110019","DOIUrl":"10.1016/j.sigpro.2025.110019","url":null,"abstract":"<div><div>In the frame of <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>−</mo><mi>α</mi><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>q</mi></mrow></msub></mrow></math></span> metric, a novel lemma of sparse representation is developed. Uniform sufficient conditions for the stable recovery of approximately <span><math><mi>k</mi></math></span>-sparse signals with partial support information are derived in different noise settings via weighted <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>−</mo><mi>α</mi><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>q</mi></mrow></msub></mrow></math></span> minimization method, and moreover, the reconstruction error bounds are precisely characterized. Specifying different values of the parameters <span><math><mrow><mi>p</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow><mo>,</mo><mspace></mspace><mi>q</mi><mo>∈</mo><mrow><mo>[</mo><mi>p</mi><mo>,</mo><mo>+</mo><mi>∞</mi><mo>]</mo></mrow></mrow></math></span> and <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>[</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>]</mo></mrow></mrow></math></span> in the new results leads to some important special cases, including the optimal results in terms of <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> convex minimization, <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> nonconvex minimization, <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>−</mo><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> minimization, <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>−</mo><mi>α</mi><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>q</mi></mrow></msub></mrow></math></span> minimization and <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>−</mo><mi>α</mi><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></math></span> minimization in the literature. A series of numerical experiments demonstrate the advantage of the new method for sparse recovery.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110019"},"PeriodicalIF":3.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820429","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":"Infrared NeRF reconstruction based on perceptual pose and high-frequency-invariant attention","authors":"Yuang Chen,&nbsp;Xiaoyu Chen,&nbsp;Canhui Zhou,&nbsp;Jing Han,&nbsp;Lianfa Bai","doi":"10.1016/j.sigpro.2025.110012","DOIUrl":"10.1016/j.sigpro.2025.110012","url":null,"abstract":"<div><div>Infrared images present significant challenges in novel view synthesis (NVS) due to low resolution and limited texture features. Additionally, the adaptive gain mechanism in infrared cameras leads to variations in the illumination across different viewpoints. These all can result in potential failures when utilizing infrared images for reconstructing Neural Radiance Fields (NeRF). To address these issues, we propose an end-to-end framework for pose estimation and rendering optimization. Specifically, perceptual pose optimization is used to estimate more accurate camera pose. To enhance the matching accuracy of multi scene corresponding points, we retain high-confidence camera poses while jointly optimizing both the scene and low-confidence poses. This allows for high-quality 3D scenes with accurate pose estimation for infrared images. The high-frequency-invariant attention module is designed to focus on the high-frequency features not easily captured and invariant edge information in infrared images by densely sampling, which can use high-frequency region to compensate for the low-frequency region differences caused by the adaptive gain mechanism. We evaluated our approach on datasets consisting of near-infrared, mid-wave infrared, and long-wave infrared images. Our method successfully reconstructs NeRF using infrared images and outperforms the state-of-the-art methods in terms of performance. The dataset is available at: <span><span>https://github.com/YuangChen111/IR-NeRF</span><svg><path></path></svg></span></div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110012"},"PeriodicalIF":3.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799461","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":"Dynamic payload adjustment in image steganography through interpolation and center folding strategies","authors":"Thanh Nhan Vo ,&nbsp;Tzu-Chuen Lu","doi":"10.1016/j.sigpro.2025.110030","DOIUrl":"10.1016/j.sigpro.2025.110030","url":null,"abstract":"<div><div>This research introduces a novel interpolation reversible data hiding (IRDH) scheme designed for secure and efficient steganographic techniques, enhancing both image quality and embedding capacity. The interpolation method involves using the original image to zoom in twice for expanding more spaces to embed the secret data. Simultaneously, the values of the original pixels (refer to “seed pixels”) are unchanged that serve for the extraction. Remarkably, the extraction process does not require a reference image, accurately retrieving both the embedded messages and the original image. The embedding procedure divides the cover image into 4 × 4 blocks. Within each block, four seed pixels remain unchanged, serving as references for recovering both the secret data and the cover image. Before embedding the data, the Multi-layer Center Folding Strategy (MCFS) encodes the secret data to minimize distortion. This strategy offers flexibility by allowing dynamic adjustments to the data embedding rate and Peak Signal-to-Noise Ratio (PSNR). The adaptability of this method lies in modifying the bit group parameter (<span><math><mi>n</mi></math></span>) within MCFS, enabling users to optimize the balance between embedding capacity and image quality. By fine-tuning <span><math><mi>n</mi></math></span>, the method achieves higher data capacity without compromising the visual integrity of the cover image. Experimental results demonstrate significant improvements in PSNR and embedding capacity compared to existing IRDH techniques.</div><div>The highlights of this paper are shown below.<ul><li><span>1.</span><span><div>Novel Multi-layer Center Folding Strategy (MCFS): Introduced a new MCFS that significantly reduces image distortion and optimizes the balance between payload capacity and image quality.</div></span></li><li><span>2.</span><span><div>Advanced Interpolation-Based Reversible Data Hiding (IRDH) Method: Developed an innovative interpolation technique that enhances embedding capacity and preserves image fidelity, achieving superior PSNR values.</div></span></li><li><span>3.</span><span><div>Dynamic Flexibility in Embedding: The proposed method allows adjustable trade-offs between embedding payload and image quality, tailored to specific application requirements.</div></span></li><li><span>4.</span><span><div>Improved Security and Robustness: Demonstrated strong resistance to steganalysis attacks (e.g., RS steganalysis and histogram analysis) while maintaining high image quality.</div></span></li><li><span>5.</span><span><div>Comprehensive Evaluation: Conducted extensive experiments on multiple datasets, achieving higher PSNR and SSIM values compared to state-of-the-art techniques, showcasing the method's efficacy and generalizability.</div></span></li></ul></div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"235 ","pages":"Article 110030"},"PeriodicalIF":3.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808789","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":"A clustering variational Bayesian Kalman filter with heavy-tailed measurement noise","authors":"Gang Wang,&nbsp;Zuxuan Zhang,&nbsp;Haihao Yang,&nbsp;Zhoubin Yao","doi":"10.1016/j.sigpro.2025.110010","DOIUrl":"10.1016/j.sigpro.2025.110010","url":null,"abstract":"<div><div>In order to solve the problem of unknown measurement noise distribution and variance in the Kalman filtering, the paper proposes a clustering variational Bayesian framework, which includes two parts: (1) a real-time clarifying method is to divide unknown heavy-tailed measurement noise into two Gaussian distributions with different parameters (means and variances), (2) an effective real-time method based Variational Bayesian (VB) is to estimate the parameters of the two Gaussian distributions. Simulations demonstrate that the proposed clustering variational Bayesian Kalman filter outperforms the existing Kalman filters in terms of both estimation accuracy and computational complexity.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110010"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760722","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":"Cauchy–Gaussian maximum mixture correntropy Kalman filter with component-by-component construction","authors":"Shungang Peng,&nbsp;Peng Cai,&nbsp;Dongyuan Lin,&nbsp;Yunfei Zheng,&nbsp;Shiyuan Wang","doi":"10.1016/j.sigpro.2025.110008","DOIUrl":"10.1016/j.sigpro.2025.110008","url":null,"abstract":"<div><div>The Kalman filter (KF) systematically optimizes the quasi-Monte Carlo (QMC) sampling points by employing a component-by-component (CBC) construction principle tailored to specific integration dimensions and accuracy requirements. This optimization enhances the approximation accuracy of Gaussian-weighted multidimensional integrals (GMIs) within the context of the KF. However, the KF based on CBC sampling points uses the minimum mean square error (MMSE) criterion, which can lead to significant estimation bias in the presence of non-Gaussian noises. To address this issue, this paper first proposes a novel Cauchy–Gaussian maximum mixture correntropy Kalman filter with component-by-component construction (CGMCKF-CBC) by the designed novel Cauchy–Gaussian maximum mixture correntropy (CGMC). Unlike the traditional maximum mixture correntropy criterion (MMCC), the CGMC uses the mixture of Cauchy kernel and Gaussian kernel as the cost function, and updates the posteriori estimates by the form of fixed-point iteration. Next, to further address the parameters selection issue existing in CGMCKF-CBC, an adaptive optimization strategy is proposed to determine appropriate parameters, resulting a variable CGMCKF-CBC (VCGMCKF-CBC). Then, the convergence analysis and complexity evaluation of CGMCKF-CBC are also conducted. Finally, two simulation examples are conducted in non-Gaussian noises environments to validate the excellent filtering accuracy and robustness of CGMCKF-CBC and VCGMCKF-CBC.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110008"},"PeriodicalIF":3.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767814","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":"Durbin tests for distributed target detection in deterministic subspace interference and noise","authors":"Peiqin Tang ,&nbsp;Xinyu Peng ,&nbsp;Hong Xu ,&nbsp;Weijian Liu ,&nbsp;Jun Liu ,&nbsp;Yongliang Wang","doi":"10.1016/j.sigpro.2025.110009","DOIUrl":"10.1016/j.sigpro.2025.110009","url":null,"abstract":"<div><div>This paper investigates the problem of distributed target detection in the high-resolution radar system, where the target is embedded in interference and Gaussian noise. The target and interference are modeled as two subspace random signals, which are assumed to belong to different deterministic and known subspaces, but with unknown coordinates. To address this issue, several adaptive detectors are proposed resorting to the Durbin criterion tailored for both homogeneous and partially homogeneous environments. At the analysis stage, numerical experiments are conducted to evaluate the performance of the detectors from various parameter perspectives. The results indicate that the proposed Durbin-based detectors outperform their competitors in some scenarios. Furthermore, these detectors can ensure the constant false alarm rate property with respect to the unknown statistics of the noise component.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110009"},"PeriodicalIF":3.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799296","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":"Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains","authors":"Clémence Prévost ,&nbsp;Pierre Chainais","doi":"10.1016/j.sigpro.2025.110001","DOIUrl":"10.1016/j.sigpro.2025.110001","url":null,"abstract":"<div><div>Tensor factorization has been steadily used to represent high-dimensional data. In particular, the canonical polyadic decomposition (CPD) is very appreciated for its remarkable uniqueness properties. However, computing the high-order CPD is challenging: numerical issues and high needs for storage and processing can make algorithms diverge. Furthermore, the recovery of the CP factors is an ill-posed problem. One way to circumvent this limitation is to exploit the equivalence between the CPD and the Tensor Train Decomposition (TTD). This paper formulates the CPD as a dimension reduction using a TTD followed by a global marginally convex optimization problem. This global optimization scheme estimates the CP factors with minimal error. The resulting approach, Dimensionality Reduction, joint Estimation of the Ambiguity Matrices and the CP FACtors (DREAMFAC), relies on a block-coordinate descent that reaches a first-order stationary point when estimating the CP factors. DREAMFAC is also shown to be an optimal estimator that reaches the corresponding constrained Cramér–Rao bound. It therefore appears as a state-of-the-art solution to estimate the best rank-<span><math><mi>K</mi></math></span> CPD of a tensor (when it exists). Its performance is illustrated on the problem of parameter estimation in a dual-polarized MIMO system. Numerical experiments show the excellent practical performance of DREAMFAC, even with very low SNR.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 110001"},"PeriodicalIF":3.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737924","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}