Signal Processing最新文献

{"title":"Moving target localization in passive distributed MIMO radar systems with unknown transmitter positions","authors":"Liehu Wu ,&nbsp;Guodong Qin ,&nbsp;Yanbin Zou ,&nbsp;Mingyi You ,&nbsp;Binhui Chen ,&nbsp;Duofang Chen","doi":"10.1016/j.sigpro.2025.110265","DOIUrl":"10.1016/j.sigpro.2025.110265","url":null,"abstract":"<div><div>This paper investigates the problem of moving target localization in passive distributed multiple-input multiple-output (MIMO) radar systems with unknown transmitter positions. Using the angle of arrival (AOA), differential time delay (DTD) and differential frequency shift (DFS) measurements, an efficient localization method is proposed to jointly estimate the positions and velocities of the target and the transmitters. The proposed method provides a closed-form solution without requiring precise time synchronization or the transmission of raw signals among receivers. Theoretical analysis shows that introducing DFS measurements can improve position estimation accuracy and that increasing the number of transmitters further enhances target localization performance. Moreover, this method is proven to achieve the Cram<span><math><mover><mrow><mtext>e</mtext></mrow><mrow><mo>́</mo></mrow></mover></math></span>r–Rao lower bound (CRLB) accuracy under small error conditions. Simulation results validate the theoretical development and show that the proposed method outperforms the existing methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110265"},"PeriodicalIF":3.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105450","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":"Distributed double proximal splitting algorithm for global constraint-coupled optimization with asynchrony and delays","authors":"Liang Ran ,&nbsp;Huaqing Li ,&nbsp;Jun Li ,&nbsp;Lifeng Zheng ,&nbsp;Run Tang ,&nbsp;Dawen Xia","doi":"10.1016/j.sigpro.2025.110275","DOIUrl":"10.1016/j.sigpro.2025.110275","url":null,"abstract":"<div><div>This paper studies a distributed constraint-coupled optimization problem in networked systems, where local objectives comprise three cost functions, two of which exhibit nonsmooth characteristics. To overcome the intractability of the summation of these nonsmooth functions, we first derive a novel local first-order sufficient condition using Lagrange duality theory. Building this foundation, we present a synchronous full-distributed double proximal splitting algorithm, in which network agents maintain private variables and collaboratively reach solutions while satisfying globally coupled linear constraints through localized information exchange. Given the issues of asynchrony and delays are non-negligible, we additionally develop an asynchronous distributed algorithm where agents independently execute computations and communications using old information for different durations. Compared to conventional synchronous approaches, this asynchronous implementation mitigates idle time caused by delays or heterogeneous node speeds. Theoretically, the convergence of the synchronous algorithm is established under local Lipschitz continuity assumption and uncoordinated constant step-size criteria. For the asynchronous variant, we prove almost sure convergence in expectation under time-varying yet bounded delays. Extensive numerical simulations on signal processing applications corroborate the theoretical findings and demonstrate algorithmic efficacy.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110275"},"PeriodicalIF":3.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105451","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":"State estimation for time-varying complex networks with Gaussian and non-Gaussian noise: Addressing data distortion and delay","authors":"Zheng Liu,&nbsp;Haibo Bao","doi":"10.1016/j.sigpro.2025.110276","DOIUrl":"10.1016/j.sigpro.2025.110276","url":null,"abstract":"<div><div>This paper investigates, for the first time, the state estimation (SE) problem in complex networks with data distortion and delay under various noise conditions. To address data distortion and delay effects caused by dynamic bias, observation fading, and random interference, this paper proposes two recursive state estimation algorithms based on Gaussian and non-Gaussian noise assumptions, respectively. Dynamic bias and observation fading are modeled using dynamic equations and a set of independent random variables, leading to the development of a new network system model. In Gaussian noise environments, an optimal data distortion and delay Kalman filter (DDKF) is proposed by improving the SE equations and error covariance bounds of the traditional delay-compensated state estimation (DCBSE) algorithm, significantly enhancing the SE accuracy. For non-Gaussian noise environments, the maximum correntropy criterion (MCC) is employed to maximize the cost function, resulting in the development of the maximum correntropy data distortion and delay Kalman filter (MCDDKF), which further improves the estimation accuracy and robustness of the DDKF under non-Gaussian noise conditions. Simulation results validate the effectiveness and applicability of both algorithms under different noise conditions.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110276"},"PeriodicalIF":3.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046140","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":"Reversible data hiding in encrypted DICOM images with fixed and block-wise pixel prediction","authors":"Remigiusz Martyniak ,&nbsp;Mariusz Dzwonkowski","doi":"10.1016/j.sigpro.2025.110287","DOIUrl":"10.1016/j.sigpro.2025.110287","url":null,"abstract":"<div><div>Reversible Data Hiding in Encrypted Images (RDHEI) is a technique that enables additional data to be embedded into encrypted images while preserving the ability to fully recover both the original image and the hidden information, making it particularly valuable for applications requiring confidentiality and integrity, such as medical imaging. This paper presents a high-capacity reversible data hiding scheme for encrypted DICOM images, addressing the unique challenges posed by their 16-bit pixel depth and structured entropy distribution. The proposed method introduces a binary decomposition strategy that separates the image into two complementary components, enabling tailored prediction techniques for each part. The first component is processed using fixed prediction—a lightweight bit-flipping mechanism, while the second employs variable block-wise model-based prediction optimized for low-error encoding. To reduce the auxiliary data overhead introduced by this two-phase preprocessing, two compression strategies—Huffman coding and Extended Run-Length Encoding—are employed. Experimental results on anonymized DICOM datasets show that the method achieves embedding rates exceeding 10 bpp while maintaining full reversibility. Comparative analysis confirms the method’s competitiveness with recent state-of-the-art RDHEI schemes. The approach is also benchmarked on non-DICOM datasets to demonstrate general applicability.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110287"},"PeriodicalIF":3.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105446","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":"Hyperspectral image compression with deep learning: A review","authors":"Fahad Saeed ,&nbsp;Shumin Liu ,&nbsp;Yelin Liu ,&nbsp;Jie Chen","doi":"10.1016/j.sigpro.2025.110270","DOIUrl":"10.1016/j.sigpro.2025.110270","url":null,"abstract":"<div><div>The integration of spectroscopy and digital imaging produces a three-dimensional data cube known as a Hyperspectral Image (HSI), where each pixel captures a spectrum spanning wavelengths from 400 nm to 2500 nm. HSIs have become increasingly indispensable across a wide range of applications, including remote sensing, military operations, medical diagnostics, food inspection and environmental monitoring. However, the rapid advancement of hyperspectral imaging technology and the growing reliance on HSIs have introduced significant challenges in storage and transmission due to their high dimensionality and substantial data volume. To address these challenges, various compression techniques have been developed, ranging from traditional methods to deep learning-based approaches. Traditional methods, such as wavelet transforms and discrete cosine transforms, have been widely used for decades but may now be deemed less effective compared to more advanced deep learning solutions. Deep learning-based techniques excel at learning complex patterns through extracting adaptive features, modeling non-linear relationships, and facilitating end-to-end learning, thereby offering superior performance in HSI compression. In this article, we provide a comprehensive review of deep learning-based HSI compression techniques, discussing their methodologies, advantages, limitations, and performance. A detailed comparison of these algorithms is systematically presented in Table <span><span>Table 5</span></span>, offering valuable insights for researchers and practitioners in the field.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110270"},"PeriodicalIF":3.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046134","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":"Scattering-guided class-irrelevant filtering for adversarially robust SAR automatic target recognition","authors":"Zhunga Liu,&nbsp;Jialin Lyu,&nbsp;Yimin Fu","doi":"10.1016/j.sigpro.2025.110273","DOIUrl":"10.1016/j.sigpro.2025.110273","url":null,"abstract":"<div><div>The vulnerability of deep neural networks (DNNs) to adversarial perturbations severely constrains their deployment in real-world applications. A common approach to defend against such perturbations is to perform input reconstruction based on image representations. However, the lack of visual intuitiveness in synthetic aperture radar (SAR) images severely complicates the reconstruction of critical target information, making the adversarial robustness of SAR automatic target recognition (ATR) systems difficult to guarantee. To address this problem, we propose a scattering-guided class-irrelevant filtering variational autoencoder (SGCIF-VAE) for adversarially robust SAR ATR. Specifically, the proposed method incorporates scattering and image-based representations to reconstruct target information from adversarial examples through feature representation and information filtering. First, strong scattering points of the target are exploited to guide the extraction of topological features, which exhibit stronger stability against adversarial perturbations than visual features. Then, a weighting reconstruction mechanism (WRM) is applied to the reconstructed image to supplement the spatial structural information. Consequently, the attention shifts induced by adversarial perturbations are effectively resisted. During training, class-relevant and class-irrelevant information are explicitly separated via a class-disentanglement variational loss (CDVL). Moreover, a bi-directional information bottleneck (BDIB) is employed to amplify the disparity in mutual information of latent variables between the input and reconstructed images, further facilitating the filtering of class-irrelevant information. Extensive experimental results on the MSTAR dataset demonstrate that SGCIF-VAE achieves superior adversarial robustness across various operating conditions. The proposed method achieves over 90% accuracy against weak perturbations and above 60% against stronger ones. The code will be released at <span><span>https://github.com/jialinlvcn/SGCIF-VAE</span><svg><path></path></svg></span> upon acceptance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110273"},"PeriodicalIF":3.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046135","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 frequency sweep synthesis for the identification of low damped systems via a narrow-band method","authors":"D. Pech,&nbsp;G. Prokop","doi":"10.1016/j.sigpro.2025.110283","DOIUrl":"10.1016/j.sigpro.2025.110283","url":null,"abstract":"<div><div>System identification via sweep excitations suffers from transients in the case of high frequency rates and low system damping. This contribution presents a novel method for a time domain generation of sweep signals to accurately estimate the frequency response function of a linear system within a desired sweep time. The approach is based on a characteristic value for determining the harmony of a signal, which was previously presented by the authors. It has been empirically found that this characteristic value is directly related to the squared derivative of the period duration of a sweep signal. Therefore, it can be used to shape a desired frequency characteristic in a way that suppresses transient effects of the system response compared to basic sweep approaches. The method is optimized to identify a single degree of freedom oscillator via particle swarm optimization. It is shown that the identification via an envelope of the system response can be enhanced by approximately 70 <span><math><mo>%</mo></math></span> compared to basic sweep signals for a weak damped oscillator. Therefore, the approach mitigates the trade-off between time requirements and accuracy of system identification via sweep excitations, if a rough estimate of the resonant frequency and the damping ratio is available.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110283"},"PeriodicalIF":3.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105529","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":"Frequency-enhanced representation and cost aggregation for multi-view stereo","authors":"Ziyuan Wang ,&nbsp;Jianzhong Cao ,&nbsp;Gaopeng Zhang ,&nbsp;Minhao Zhang ,&nbsp;Boxue Zhang ,&nbsp;Weining Chen ,&nbsp;Xin Ma ,&nbsp;Feng Wang","doi":"10.1016/j.sigpro.2025.110280","DOIUrl":"10.1016/j.sigpro.2025.110280","url":null,"abstract":"<div><div>Cascade-based multi-view stereo (MVS) methods demonstrate exceptional flexibility and efficiency in 3D reconstruction tasks. However, existing methods predominantly focus on pixel-wise correlations in the spatial domain while overlooking the critical role of frequency-domain information essential for modeling challenging scenarios, leading to suboptimal 3D geometric reconstruction. Furthermore, downsampling during multi-scale feature extraction may lead to the loss of critical spatial details, undermining the fidelity of depth estimation in visually degraded scenes. In this paper, we propose FA-MVS, a framework that explicitly incorporates frequency information into multi-scale depth estimation to enhance frequency awareness. Specifically, we propose a frequency-enhanced feature extractor, where frequency representations fused with spatial depth priors are progressively refined to bolster robustness against frequency-sensitive variations. Meanwhile, we propose a frequency-aware cost aggregation module that integrates frequency cues into the cost volume, enabling precise capture of fine details in boundaries and occluded regions. Extensive experiments conducted on the DTU, Tanks and Temples, as well as the challenging ETH3D datasets, demonstrate that our method achieves competitive performance compared to existing advanced approaches while exhibiting strong generalization capability.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110280"},"PeriodicalIF":3.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046138","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":"Simultaneous multi-beam space-time-Doppler coherent integration for high-speed moving target detection in passive bistatic array radar","authors":"Aodi Yang ,&nbsp;Haihong Tao ,&nbsp;Le Wang ,&nbsp;Fusen Yang ,&nbsp;Xiaoyu Xu ,&nbsp;Huihui Ma","doi":"10.1016/j.sigpro.2025.110274","DOIUrl":"10.1016/j.sigpro.2025.110274","url":null,"abstract":"<div><div>High-speed moving targets (HSMTs) pose challenges for passive bistatic array radar (PBAR). When HSMTs deviate from the transmit-receive beam’s irradiation region, PBAR cannot effectively receive echo energy over an extended priod, failing to balance coherent accumulation time and goniometric accuracy. A solution is to use a satellite’s wide beam and multiple adjacent narrow receiving beams from a ground-based silent array radar. However, during long-time accumulation, HSMTs’ radial and tangential velocities cause range migration (RM), pitch beam migration (PBM), azimuth beam migration (ABM), and Doppler frequency migration (DFM). These scatter HSMT echo energy, degrading Long-Time Coherent Integration (LTCI) performance. This paper proposes a simultaneous multi-beam space-time-Doppler (STD) algorithm. First, it employs a joint beam migration correction mechanism combining phase correction and cancellation to focus multi-beam energy on the beam unit at the HSMT’s terminal moment, then utilizes the Frequency Domain Dechirp Fourier Transform (FDDFT) to handle RM and DFM, and tunes the echo Doppler and modulation frequencies to the same domain to complete coherent integration, and finally achieves detection and localization of HSMTs via the multi-beam joint centroid angle measurement. Simulations verify the superiority of the proposed STD algorithm in high-speed target detection and coherent integration.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110274"},"PeriodicalIF":3.6,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046136","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":"Elastic-Net regularized two-dimensional canonical correlation analysis for robust underwater sonar image classification","authors":"Yule Chen,&nbsp;Hong Liang,&nbsp;Lei Yue,&nbsp;Siyuan Song","doi":"10.1016/j.sigpro.2025.110272","DOIUrl":"10.1016/j.sigpro.2025.110272","url":null,"abstract":"<div><div>For sonar image classification, it is critical to obtain effective representations from sensor data impaired by underwater interference. To address the loss of spatial structure inherent in vectorized canonical correlation analysis (CCA), we propose a two–dimensional Elastic Net regularized CCA (2D-ECCA), a sliding window formulation that preserves highlight–shadow geometry while embedding a mixed penalty <span><math><mrow><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub><mspace></mspace><mo>+</mo><mspace></mspace><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> directly inside the correlation objective. The proposed 2D-ECCA operates directly on local two-dimensional windows, thereby preserving spatial correlations as well as discriminative texture cues. Embedding an Elastic Net penalty in the correlation maximization objective further enhances robustness, suppresses overfitting, and yields more interpretable projections. A dedicated alternating minimization solver combines forward–backward updates for the projection blocks with a closed-form step for the shared latent matrix, guaranteeing monotone descent and complexity per iteration. Extensive experimental analysis on two public datasets and two experimental datasets validate the effectiveness and efficiency of the proposed algorithm. A field trial with an Oculus MT750d forward-looking sonar mounted on an underwater unmanned vehicle further confirms real-time capability (0.04 GFLOPs, 13 ms per frame) and 87.8 % accuracy at 1.2 MHz over the 40 m range. It is suitable for real-time deployment on resource-limited underwater platforms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110272"},"PeriodicalIF":3.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046137","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}