Signal Processing最新文献_第6页

Priori-assisted soft actor–critic based interrupted sampling repeater jamming method 基于优先辅助软评价的中断采样中继器干扰方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-28 DOI: 10.1016/j.sigpro.2025.110259

Jiaqi Tan , Tianpeng Liu , Weidong Jiang , Dewang Wang , Zhen Liu , Zhongguo Wu

{"title":"Priori-assisted soft actor–critic based interrupted sampling repeater jamming method","authors":"Jiaqi Tan , Tianpeng Liu , Weidong Jiang , Dewang Wang , Zhen Liu , Zhongguo Wu","doi":"10.1016/j.sigpro.2025.110259","DOIUrl":"10.1016/j.sigpro.2025.110259","url":null,"abstract":"<div><div>Interrupted sampling repeater jamming (ISRJ) is an electronic countermeasure widely used in airborne self-defense jamming equipment, with its performance heavily dependent on the jamming parameters. In non-cooperative and dynamic countermeasure scenarios, existing jamming decision-making methods that rely heavily on expert knowledge bases and precise mathematical models are prone to failure, making it challenging to achieve intelligent and adaptive jamming. Furthermore, due to the multi-dimensional and continuous nature of parameter spaces, these methods tend to converge slowly. To address these issues, we propose a priori-assisted soft actor–critic (SAC) based ISRJ method. In our method, we first model the ISRJ decisions throughout the entire penetration process as a Markov decision process (MDP) and design distinct reward functions for both cooperative and non-cooperative scenarios. We then utilize the SAC algorithm to learn the optimal strategy in an unknown, dynamic environment while efficiently managing large state–action spaces. To accelerate convergence while effectively avoiding potential local optima, we propose the priori-assisted SAC algorithm to solve the above MDP. Compared to the classical SAC, the proposed algorithm integrates expert prior information, which assists the agent in exploring more effectively, thereby improving both the efficiency and quality of policy learning. Extensive simulation results confirm the superiority of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110259"},"PeriodicalIF":3.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019263","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}

引用次数: 0

Time-varying thresholds with Gaussian smoothing for one-bit DOA estimation in unequal power signals 基于高斯平滑的时变阈值非等功率信号的位DOA估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-27 DOI: 10.1016/j.sigpro.2025.110258

Anqi Yan, Yan Zhou, Yaxin Xiao, Siyu Yang, Chuangrui Meng

{"title":"Time-varying thresholds with Gaussian smoothing for one-bit DOA estimation in unequal power signals","authors":"Anqi Yan, Yan Zhou, Yaxin Xiao, Siyu Yang, Chuangrui Meng","doi":"10.1016/j.sigpro.2025.110258","DOIUrl":"10.1016/j.sigpro.2025.110258","url":null,"abstract":"<div><div>In the field of wireless localization, one-bit quantization faces significant challenges in unequal power signal scenarios due to fixed thresholds and amplified quantization noise. This paper proposes a time-varying (TV) threshold strategy combined with Gaussian smoothing to enhance the Direction of Arrival (DOA) estimation. The method dynamically divides time windows and selects the median value of sub-intervals as the quantization threshold, enabling the algorithm to adapt to signal power variations and reduce interference between strong and weak signals. By reconstructing the covariance matrix using Newton’s iteration method and the gradient descent method, the signal subspace can be accurately recovered. Gaussian smoothing further suppresses high-frequency noise, enhancing the robustness while preserving the angular resolution of the Multiple Signal Classification (MUSIC) algorithm. Experimental results show that under a low Signal-to-Noise Ratio (SNR, −5 dB), compared with the unsmoothed method, the Root Mean Square Error (RMSE) is reduced by 25.6%, and sub-degree-level accuracy can be achieved even when strong and weak signals coexist. This approach provides a more effective solution for one-bit DOA estimation in practical unequal power signal scenarios and promotes the development of wireless localization.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110258"},"PeriodicalIF":3.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920194","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}

引用次数: 0

Depth-map-awared underwater image restoration using variational guided regularization 基于变分制导正则化的深度地图感知水下图像恢复

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-25 DOI: 10.1016/j.sigpro.2025.110241

Biao Ye, Liming Tang, Jiacheng Wu, Zhuang Fang

{"title":"Depth-map-awared underwater image restoration using variational guided regularization","authors":"Biao Ye, Liming Tang, Jiacheng Wu, Zhuang Fang","doi":"10.1016/j.sigpro.2025.110241","DOIUrl":"10.1016/j.sigpro.2025.110241","url":null,"abstract":"<div><div>Underwater images often suffer from degradation due to the absorption and scattering of light, resulting in low visibility and turbid visual effects. To address this problem, we propose a depth-map-awared variational regularization model for underwater image restoration. First, we utilize deep learning techniques to preliminarily estimate a depth map based on the underwater image light attenuation prior. Next, we establish a variational regularization model that simultaneously refines the estimated depth map and restores the underwater images. The proposed model incorporates a total variation regularization term for the restored image and a guided regularization term for the depth map. This guided regularization term constrains the depth map to approximate the initially estimated depth while also enforcing fractional-order (<span><math><mrow><mi>s</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>) smoothness. Furthermore, we demonstrate the convexity of the model, ensuring the existence and uniqueness of solutions. Finally, we employ the alternating direction method of multipliers (ADMM) to solve the proposed model. Extensive experiments show that our model outperforms several state-of-the-art restoration techniques, with significant improvements in image quality as measured by the no-reference underwater color image quality evaluation (UCIQE) and fog-aware density evaluator (FADE).</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110241"},"PeriodicalIF":3.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913459","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}

引用次数: 0

APDMs: Adversarial purification diffusion models for automatic modulation classification 用于自动调制分类的对抗纯化扩散模型

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-23 DOI: 10.1016/j.sigpro.2025.110249

Hang Zhang , Wenrui Ding , Duona Zhang , Jing Xiao , Zeqi Shao , Baihe Chen

引用次数: 0

Robust spectrum sensing for unknown heteroscedastic noise via covariance-based convolutional neural network 基于协方差的卷积神经网络对未知异方差噪声的鲁棒频谱感知

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-23 DOI: 10.1016/j.sigpro.2025.110254

Guiju Zhong , Zhen-Qing He , Zhi-Ping Shi , Hongbin Li

{"title":"Robust spectrum sensing for unknown heteroscedastic noise via covariance-based convolutional neural network","authors":"Guiju Zhong , Zhen-Qing He , Zhi-Ping Shi , Hongbin Li","doi":"10.1016/j.sigpro.2025.110254","DOIUrl":"10.1016/j.sigpro.2025.110254","url":null,"abstract":"<div><div>This paper addresses the problem of spectrum sensing using multi-antenna cognitive receivers in unknown heteroscedastic noise environment, where the noise variances may vary in space and time. Specifically, we propose a robust data-driven spectrum sensing approach using a covariance-based deep convolutional neural network (CNN). In particular, we take the sample covariance matrix (SCM) with its unknown noise variances being well suppressed as the input of CNN to train a robust and generalized test statistic against the heteroscedastic noise. Meanwhile, we design a CNN architecture with a strided convolution layer to retain detailed feature information of the noise-suppressed SCM and a batch normalization layer to accelerate the CNN training. Various simulation results demonstrate that the proposed method attains an accurate detection performance and adapts well to different types of heteroscedastic noise. Particularly, the proposed approach achieves detection probabilities exceeding 99% and 95% under worst noise power ratios of 5 and 80, respectively, when the signal-to-noise ratio is <span><math><mrow><mo>−</mo><mn>18</mn></mrow></math></span> dB with a false alarm probability of 10%.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110254"},"PeriodicalIF":3.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907094","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}

引用次数: 0

Anti-interrupted sampling repeater jamming via linear canonical Wigner distribution lightweight LFM detection 基于线性正则维格纳分布轻量LFM检测的抗干扰采样中继器

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-22 DOI: 10.1016/j.sigpro.2025.110230

Jia-Mian Li, Bing-Zhao Li

{"title":"Anti-interrupted sampling repeater jamming via linear canonical Wigner distribution lightweight LFM detection","authors":"Jia-Mian Li, Bing-Zhao Li","doi":"10.1016/j.sigpro.2025.110230","DOIUrl":"10.1016/j.sigpro.2025.110230","url":null,"abstract":"<div><div>Interrupted sampling repeater jamming (ISRJ) poses a serious threat to radar target detection. Traditional time-frequency (TF) domain anti-jamming methods are prone to TF aliasing in multi-component signal scenarios, and cannot effectively suppress ISRJ with energy close to the real target under low signal-to-noise ratio (SNR) conditions. To address these challenges, this paper proposes an anti-jamming method based on generalized linear canonical Wigner distribution (GLWD) line detection. By setting the parameters reasonably, the TF image of GLWD can have excellent TF resolution and energy concentration, greatly improving the signal separation and SNR. Furthermore, in order to enhance the detection capability of the target LFM signal, the existing mobile line segment detection (M-LSD) is improved and the mobile long line segment detection (M-LLSD) is proposed. M-LLSD can detect the target signal more easily and reduce the sensitivity to the jamming signal, so as to efficiently and accurately extract the TF position information of the target signal. Finally, a TF filter is constructed based on the mapping between GLWD and short-time Fourier transform (STFT), performing filtering in the STFT domain to suppress jamming. Simulations and experiments show that the method can effectively suppress such difficult-to-distinguish jamming and is suitable for real-time radar anti-jamming with good robustness.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110230"},"PeriodicalIF":3.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902879","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}

引用次数: 0

Orthogonalized variational mode decomposition 正交变分模态分解

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-22 DOI: 10.1016/j.sigpro.2025.110251

Himpu Marbona , Daniel Rodríguez , Alejandro Martínez-Cava , Eusebio Valero

引用次数: 0

Riemannian gradient deep network for joint waveform and filter optimization in MIMO radar against chopping forwarding jamming and clutter MIMO雷达抗斩波转发干扰和杂波联合波形和滤波器优化的黎曼梯度深度网络

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-22 DOI: 10.1016/j.sigpro.2025.110257

Yizhen Jia, Hui Chen, Bang Huang, WenKai Jia, Wen-Qin Wang

{"title":"Riemannian gradient deep network for joint waveform and filter optimization in MIMO radar against chopping forwarding jamming and clutter","authors":"Yizhen Jia, Hui Chen, Bang Huang, WenKai Jia, Wen-Qin Wang","doi":"10.1016/j.sigpro.2025.110257","DOIUrl":"10.1016/j.sigpro.2025.110257","url":null,"abstract":"<div><div>With the rise of digital radio frequency memory technology, active deception jamming poses a significant threat to radar systems, especially in detecting targets amid mainlobe jamming and non-Gaussian clutter. Traditional methods like space–time matched filtering struggle in such scenarios. This study introduces the Riemannian gradient deep network (RGDN), a framework for joint optimization of transmit waveforms and receive filters to improve target detection. Unlike conventional signal-to-clutter noise ratio (SCNR) maximization, RGDN leverages information geometry to maximize the Kullback–Leibler Divergence (KLD) between targets and clutter. By modeling non-Gaussian data with a Gaussian mixture distribution and constructing a Riemannian manifold, the framework achieves effective jamming suppression through receive filter term in the loss function, minimizing jamming effects while enhancing target-clutter distinguishability. To address non-convex optimization, Riemannian gradient descent is integrated into a deep network. Numerical experiments show that RGDN achieves superior detection performance compared to SCNR maximization method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110257"},"PeriodicalIF":3.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902878","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}

引用次数: 0

Dual-stream temporal–spectral framework for accurate eye movement event detection 精确眼动事件检测的双流时间-光谱框架

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-22 DOI: 10.1016/j.sigpro.2025.110252

Yang Zheng , Zhiyong Yu , Ruimin Li , Jimin Liang , Kaitai Guo

{"title":"Dual-stream temporal–spectral framework for accurate eye movement event detection","authors":"Yang Zheng , Zhiyong Yu , Ruimin Li , Jimin Liang , Kaitai Guo","doi":"10.1016/j.sigpro.2025.110252","DOIUrl":"10.1016/j.sigpro.2025.110252","url":null,"abstract":"<div><div>Accurate detection of eye movement events is crucial for unraveling complex visual behaviors and driving advancements in neuroscience and cognitive research. Deep learning approaches have demonstrated exceptional potential, surpassing traditional and machine learning methods by effectively capturing and modeling intricate patterns in eye movement data. However, existing deep learning methods face limitations, with small receptive fields struggling to capture cross-boundary information and large receptive fields failing to precisely delineate event borders. To address this, we propose GazeFusion, a novel dual-stream framework that integrates global temporal and local spectral features for comprehensive eye movement event detection. The framework comprises a Mamba Temporal Feature Extractor for long-range temporal dependencies, a CNN Spectral Feature Extractor for explicit frequency-domain representation, a Contextualizer leveraging attention mechanisms for global–local temporal–spectral feature fusion, and a Sequential Enhancer for refined sequential modeling. Experimental results on three publicly available datasets consistently demonstrate that GazeFusion outperforms state-of-the-art methods across all categories, offering superior accuracy and robust boundary detection.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110252"},"PeriodicalIF":3.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988414","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}

引用次数: 0

An efficient MD-GRao algorithm for quickest change detection in sensor networks with unknown post-change parameters 具有未知后变化参数的传感器网络快速变化检测的MD-GRao算法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2025-08-22 DOI: 10.1016/j.sigpro.2025.110243

Peichao Wang , Bingqian Yu , Yuxuan Liu , Yangyang Wang

{"title":"An efficient MD-GRao algorithm for quickest change detection in sensor networks with unknown post-change parameters","authors":"Peichao Wang , Bingqian Yu , Yuxuan Liu , Yangyang Wang","doi":"10.1016/j.sigpro.2025.110243","DOIUrl":"10.1016/j.sigpro.2025.110243","url":null,"abstract":"<div><div>Consider a quickest change detection (QCD) problem in sensor networks for monitoring a sudden parameters change in signals with unknown post-change parameters, where the change may be subtle. Traditional approaches, such as the generalized likelihood ratio test-based QCD (GLRT-QCD), are often computationally prohibitive for real-time applications. The Rao test-based QCD (Rao-QCD), though simpler, results in higher false alarm rates and longer delays. To address these limitations, we propose a modified drift-oriented generalized Rao (MD-GRao) algorithm, which strikes a well-balanced tradeoff between computational complexity and detection effectiveness, and can be applied to general cases. For the first time, we analyze the drift property of Rao-QCD and reveal the monotonically increasing nature of its statistic. Based on this insight, we propose a dynamic window update strategy to efficiently estimate unknown parameters and develop a recursive update approach that incorporates a negative drift mechanism, enabling rapid identification of the potential change. Theoretical analysis establishes the asymptotic performance of the proposed algorithm, while comprehensive numerical evaluations of heart rate change detection in a radar-based sensor network demonstrate its superior computational efficiency over traditional methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110243"},"PeriodicalIF":3.6,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907095","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}

引用次数: 0