Signal Processing最新文献_第7页

Infrared and visible image fusion via spatial-frequency edge-aware network 基于空频边缘感知网络的红外与可见光图像融合

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-13 DOI: 10.1016/j.sigpro.2025.110441

Shuohui Li , Qilei Li , Mingliang Gao , Lucia Cascone , Dan Zhang

The objective of combining infrared with visible images lies in merging essential visual data from both sources to produce an enhanced output. Existing fusion methods predominantly operate within the spatial domain, while ignoring valuable data that could be extracted from the frequency domain. Therefore, the fusion performance remains suboptimal. To overcome this drawback, we introduce the Spatial-Frequency Edge-Aware Network(SFEANet) model, which employs a parallel dual-branch structure that simultaneously processes spatial and frequency domain information. The spatial fusion branch utilizes the Edge Feature Extraction(EFE) block and the Self Attention(SA) block to capture and integrate key features across both image types. The frequency-domain fusion branch first applies the Fast Fourier Transform(FFT) for domain conversion, which transforms the input into spectral representations. Subsequently, it performs interactive operations on their amplitude and phase components to enable cross-modal feature integration. The fused features are ultimately reconstructed in the spatial domain through the Inverse Fast Fourier Transform (IFFT). Comprehensive experiments conducted on three public benchmarks demonstrate the superior performance of SFEANet across multiple quantitative measures and perceptual quality assessments. The implementation can be accessed via https://github.com/lishuohui123/SFEANet.

将红外图像与可见光图像相结合的目的在于合并来自两个来源的基本视觉数据，以产生增强的输出。现有的融合方法主要在空间域内操作，而忽略了可以从频域提取的有价值的数据。因此，融合性能仍然是次优的。为了克服这一缺点，我们引入了空间-频率边缘感知网络（SFEANet）模型，该模型采用并行双分支结构同时处理空间和频域信息。空间融合分支利用边缘特征提取（EFE）块和自关注（SA）块来捕获和整合两种图像类型的关键特征。频域融合分支首先应用快速傅里叶变换（FFT）进行域转换，将输入转换为频谱表示。随后，它对它们的幅度和相位分量进行交互操作，以实现跨模态特征集成。最后通过快速傅里叶反变换（IFFT）在空间域中重构融合特征。在三个公共基准上进行的综合实验表明，SFEANet在多个定量测量和感知质量评估方面表现优异。实现可以通过https://github.com/lishuohui123/SFEANet访问。

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引用次数: 0

Improved ADTFD-class algorithms for HFM signals based on direction extension using an energy concentration criterion 基于能量集中准则的HFM信号方向扩展改进adtfd类算法

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-11-29 DOI: 10.1016/j.sigpro.2025.110422

Shuai Yao , Jinyu Lin , Yixuan Zhang , Xincheng Zhao , Zixu Wang , Qisong Wu , Chaochao Wang

Fast adaptive directional time-frequency distribution (F-ADTFD) represents an efficient variant of ADTFD, achieving a balance between low computational complexity and high-performance time-frequency analysis. However, its direction estimation methodology may lead to the loss of auto-term directions when employed for analyzing hyperbolic frequency modulated (HFM) signals, where auto-term directions exhibit time-varying characteristics. To address this dilemma, a novel direction extension framework guided by an energy concentration criterion for HFM signals is proposed in this paper. The framework operates in two sequential stages: first, identifying potential auto-term directions of the HFM signal in (ν, τ) plane, and second, extending these directions by using the ratio of norms concentration measure in (t, f) plane. Leveraging the aforementioned framework, two specialized time-frequency analysis algorithms are developed by integrating with fast ADTFD (F-ADTFD) and locally optimized ADTFD (LO-ADTFD), respectively, namely improved F-ADTFD (IF-ADTFD) and fast LO-ADTFD (FLO-ADTFD). Both simulation and experimental results have verified that compared with F-ADTFD, IF-ADTFD mitigates the risk of signal component loss during HFM signal processing. Additionally, FLO-ADTFD achieves performance comparable to LO-ADTFD with 9–54 % reduced computational complexity, demonstrating an average reduction of 40 % across simulations and experiment.

快速自适应定向时频分布（F-ADTFD）是ADTFD的一种有效变体，在低计算复杂度和高性能时频分析之间取得了平衡。然而，其方向估计方法在分析双曲调频（HFM）信号时可能导致自动项方向的丢失，其中自动项方向具有时变特性。为了解决这一难题，本文提出了一种基于能量集中准则的高频调频信号方向扩展框架。该框架分为两个连续的阶段：首先，确定（ν, τ）平面中高频调频信号的潜在自动项方向，其次，通过使用（t, f）平面中的规范浓度比测量来扩展这些方向。利用上述框架，通过集成快速ADTFD （F-ADTFD）和局部优化ADTFD (LO-ADTFD)，分别开发了两种专门的时频分析算法，即改进的F-ADTFD （IF-ADTFD）和快速的LO-ADTFD （fl -ADTFD）。仿真和实验结果均证实，与F-ADTFD相比，IF-ADTFD降低了高频调频信号处理过程中信号分量丢失的风险。此外，FLO-ADTFD的性能与LO-ADTFD相当，计算复杂度降低了9 - 54%，在模拟和实验中平均降低了40%。

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引用次数: 0

A survey on deep learning enabled automatic modulation classification methods: Data representations, model structures, and regularization techniques 深度学习自动调制分类方法综述：数据表示、模型结构和正则化技术

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-09 DOI: 10.1016/j.sigpro.2025.110444

Xinyu Tian , Qinghe Zheng , Binglin Li , Dali Qiao , Kan Yu , Zhiqing Wei , Bin Li , Hao Jiang , Xingwang Li , Yun Lin , Guan Gui

Nowadays, intelligent wireless communications have sparked developments in multiple fields due to their ultra-high speed, low latency, and large-scale connectivity capabilities. As a key technique in cognitive communications, automatic modulation classification (AMC) aims to identify the modulation scheme of unknown received signals. AMC has played an important role in both military and civilian applications. Besides, the rapid development of artificial intelligence algorithms represented by deep learning (DL) has brought new opportunities to AMC. In this survey, we investigated a series of DL enabled AMC methods, including key technology, performance, advantages, challenges, and future key development directions. The technical details of various AMC methods are introduced, such as data representation, model structure, and regularization technique in the training process. Extensive experimental results of state-of-the-art DL enabled AMC methods on public or simulated datasets have been compared and analyzed. Despite the achievements that have been made, there are still limitations of existing methods, including generalization capability, inference efficiency, model complexity, and robustness to changing communication parameters. Finally, we have summarized the main challenges faced by DL enabled AMC methods and key future research directions. Critical theoretical foundations and technical routes are envisioned to stimulate core ideas for improving the AMC performance.

如今，智能无线通信因其超高速、低延迟和大规模连接能力而引发了多个领域的发展。自动调制分类（AMC）是认知通信中的一项关键技术，其目的是识别未知接收信号的调制方式。AMC在军事和民用领域都发挥了重要作用。此外，以深度学习（deep learning， DL）为代表的人工智能算法的快速发展也给AMC带来了新的机遇。在本次调查中，我们研究了一系列基于深度学习的AMC方法，包括关键技术、性能、优势、挑战和未来的关键发展方向。介绍了各种AMC方法的技术细节，如数据表示、模型结构、训练过程中的正则化技术等。对公共或模拟数据集上最先进的DL启用AMC方法的大量实验结果进行了比较和分析。尽管已经取得了一些成就，但现有方法仍然存在局限性，包括泛化能力、推理效率、模型复杂性以及对通信参数变化的鲁棒性等。最后，总结了基于深度学习的AMC方法面临的主要挑战和未来的重点研究方向。设想了关键的理论基础和技术路线，以激发提高AMC性能的核心思想。

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引用次数: 0

A novel distance-velocity estimation algorithm for FMCW-LiDAR based on trapezoid wave 基于梯形波的FMCW-LiDAR距离-速度估计新算法

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-02 DOI: 10.1016/j.sigpro.2025.110434

Wenyan Hong , Shiyi Shen , Shihuang Wu , Huiying Li , Mengting Lian , Yixiong Zhang , Caipin Li , Jianyang Zhou

Frequency Modulation Continuous Wave - Light Detection and Ranging (FMCW-LiDAR) can simultaneously estimate the distance and Doppler information of a target in a single measurement. The traditional FMCW-LiDAR employs a triangular wave and I-channel sampling, resulting in a symmetric spectrum. However, when the absolute value of the target Doppler frequency exceeds the distance frequency, there is an issue of distance-velocity coupling. In addition, when the laser beam passes through the edge of a target, the LiDAR may receive two echoes from different targets, making target frequency pairing difficult. In this paper, we propose a novel distance-velocity estimation algorithm based on trapezoid wave. The proposed method utilizes a constant frequency part of trapezoid wave to estimate Doppler frequency. Then, the absolute value of the Doppler frequency is used as a criterion for negative frequency estimation and target matching. Simulation results show that the distance-velocity frequency estimation range of the proposed method is twice as high as that of the triangular wave method and the utilization rate of time resources has increased by 25 % compared with the variable-frequency triangular wave.

调频连续波光探测和测距（FMCW-LiDAR）可以在一次测量中同时估计目标的距离和多普勒信息。传统的FMCW-LiDAR采用三角波和i通道采样，从而产生对称频谱。然而，当目标多普勒频率的绝对值超过距离频率时，就会出现距离-速度耦合问题。此外，当激光束穿过目标边缘时，激光雷达可能接收到来自不同目标的两个回波，使目标频率配对变得困难。本文提出了一种新的基于梯形波的距离-速度估计算法。该方法利用梯形波的恒频部分来估计多普勒频率。然后，将多普勒频率的绝对值作为负频率估计和目标匹配的准则。仿真结果表明，该方法的距离-速度频率估计范围是三角波法的2倍，时间资源利用率比变频三角波法提高了25%。

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引用次数: 0

Underwater acoustic signal denoising with diffusion-based generative models 基于扩散生成模型的水声信号去噪

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-09 DOI: 10.1016/j.sigpro.2025.110430

Boqing Zhu , Yanxin Ma , Zemin Zhou, Wei Guo, Jiahua Zhu, Xiaoqian Zhu

In the work, we proposing a novel generative denoising framework for underwater acoustic denoising based on the diffusion model. Underwater acoustic signal denoising is a challenging task due to the complex, non-stationary, and often non-Gaussian nature of ambient ocean noise. Unlike conventional deep learning approaches that rely heavily on supervised learning and prior knowledge of noise distributions, our method leverages a score-based diffusion model formulated through stochastic differential equations, enabling purely generative training without explicit noise assumptions. Furthermore, we extend the diffusion process and score-matching objective into the complex domain to incorporate phase information, which is essential for reconstructing high-fidelity underwater signals. Extensive experiments on real-world underwater datasets under both simulated and real ambient noise demonstrate the superiority and generalization ability of our approach compared to existing methods.

本文提出了一种新的基于扩散模型的水声去噪生成框架。由于海洋环境噪声的复杂性、非平稳性和非高斯性，水声信号去噪是一项具有挑战性的任务。与传统的深度学习方法严重依赖于监督学习和噪声分布的先验知识不同，我们的方法利用了一个基于分数的扩散模型，该模型是通过随机微分方程制定的，可以在没有明确噪声假设的情况下进行纯粹的生成训练。此外，我们将扩散过程和分数匹配目标扩展到复域，以纳入相位信息，这是重建高保真水下信号所必需的。在模拟和真实环境噪声下对真实水下数据集进行的大量实验表明，与现有方法相比，我们的方法具有优越性和泛化能力。

引用次数: 0

Gridless DOA estimation for arbitrary array geometries based on maximum likelihood 基于极大似然的任意阵列几何的无网格DOA估计

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-11-24 DOI: 10.1016/j.sigpro.2025.110415

Tianjun Zhou , Yuan Cao, Qunfei Zhang

This paper presents a gridless maximum likelihood (ML) direction-of-arrival (DOA) estimation method for arbitrary array geometries. The approach parameterizes the likelihood function of the received signal covariance matrix and formulates a structured optimization problem to recover a positive semidefinite Hermitian Toeplitz matrix encoding the sources’ azimuth and power information. Gridless DOA estimates are then extracted from this matrix via ESPRIT or Vandermonde decomposition. To solve the resulting nonconvex ML problem, two iterative algorithms are developed: a difference-of-convex programming method with convergence guarantees and a Quasi-Newton scheme that reduces computational complexity while maintaining accuracy. Simulations with an eight-element uniform circular array compare the proposed method with MUSIC, SPICE, SBL, OGSBI, and SPICE-GL, demonstrating effective mitigation of grid-mismatch errors and superior or comparable estimation accuracy under various scenarios.

提出了一种任意阵列几何形状的无网格最大似然到达方向估计方法。该方法将接收信号协方差矩阵的似然函数参数化，构造了一个结构化优化问题，以恢复编码信号源方位角和功率信息的正半定厄米图普利兹矩阵。然后通过ESPRIT或Vandermonde分解从该矩阵中提取无网格DOA估计。为了解决由此产生的非凸ML问题，开发了两种迭代算法：具有收敛保证的凸差分规划方法和在保持精度的同时降低计算复杂度的准牛顿方案。在八元均匀圆形阵列的仿真中，将所提出的方法与MUSIC、SPICE、SBL、OGSBI和SPICE- gl进行了比较，证明了该方法有效地缓解了网格不匹配误差，并且在各种场景下具有优越或相当的估计精度。

引用次数: 0

Riemannian meta-optimization for transmit-receive joint design towards smeared spectrum jamming suppression 针对模糊频谱干扰抑制的收发联合设计黎曼元优化

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-11-25 DOI: 10.1016/j.sigpro.2025.110414

Xiangfeng Qiu , Weidong Jiang , Xinyu Zhang , Yongxiang Liu , Symeon Chatzinotas , Fulvio Gini , Maria Sabrina Greco

The smeared spectrum (SMSP) jamming technique generates dense comb-shaped false targets at the receiver, complicating the detection of the target of interest. This paper investigates countermeasures for SMSP jamming in a multiple-input multiple-output (MIMO) radar system through the joint optimization of the transmitter and receiver. Specifically, we formulate the transmit-receive design problem as a jointly constrained optimization problem with the objectives of minimizing waveform sidelobes, jamming energy, and mutual interference across different waveform-filter pairs and SMSP-filter pairs. To overcome the difficulties posed by the non-convex constraints, we reformulate the original constrained optimization problem into an unconstrained problem within the Riemannian manifold space. We then introduce a Riemannian meta-optimization (RMO) approach that integrates manifold optimization principles with meta-learning techniques. This RMO method employs meta-optimizers to update iteratively transmit waveforms and receive filters via implicit gradient descent, which ensures that the optimization variables are faithful to the constrained spaces during iterations. Parameterized long-short-term memory (LSTM) based meta-networks are developed to learn and apply an alternative, adaptive optimization strategy. Notably, the method initializes randomly for each anti-SMSP problem instance and updates iteratively, enabling effective deployment in various jamming scenarios without requiring labeled training data. Numerical simulations allow for achieving superior performance in SMSP jamming suppression.

SMSP干扰技术在接收端产生密集的梳状假目标，使目标检测变得复杂。本文通过对发射机和接收机的联合优化，研究了多输入多输出（MIMO）雷达系统中SMSP干扰的对策。具体来说，我们将收发设计问题描述为一个联合约束优化问题，其目标是最小化波形副瓣、干扰能量以及不同波形滤波器对和smsp滤波器对之间的相互干扰。为了克服非凸约束所带来的困难，我们将原来的约束优化问题重新表述为黎曼流形空间内的无约束问题。然后，我们介绍了一种黎曼元优化（RMO）方法，该方法将流形优化原理与元学习技术相结合。该方法采用元优化器通过隐式梯度下降迭代更新发射波形和接收滤波器，保证了迭代过程中优化变量忠实于约束空间。基于参数化长短期记忆（LSTM）的元网络被开发用来学习和应用一种替代的自适应优化策略。值得注意的是，该方法针对每个anti-SMSP问题实例进行随机初始化并迭代更新，从而能够在不需要标记训练数据的情况下有效地部署在各种干扰场景中。数值模拟表明，该方法在抑制SMSP干扰方面具有优异的性能。

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引用次数: 0

Low-resolution MIMO radar waveform design for super-resolution DOA estimation 低分辨率MIMO雷达波形设计的超分辨率DOA估计

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-11-22 DOI: 10.1016/j.sigpro.2025.110413

Ping Huang , Wenjun Wu , Bo Tang , Mojtaba Soltanalian , M.R. Bhavani Shankar

Employing low-resolution (e.g., one-bit) digital-to-analog converters (DACs) in MIMO radar systems can achieve substantial reductions in hardware cost and power consumption without significantly compromising performance, particularly for large-scale antenna arrays. In this paper, we consider the design of low-resolution waveforms to enhance the target localization performance of multiple-input-multiple-output (MIMO) radar systems. We employ the asymptotic mean square errors (MSE) of the direction of arrival (DOA) obtained by the multiple signal classification (MUSIC) as the design metric. Under the assumption that the MIMO radar receive array is a standard uniform linear array (i.e., the inter-element array spacing is equal to half the wavelength), we show that the design metric has a compact expression and facilitates the theoretical analysis of the performance bound. To design low-resolution waveforms that can be generated by low-bit DACs, we propose two iterative approaches, which are based on the block coordinate descent method and Dinkelbach’s transform. We prove that the proposed approaches have guaranteed convergence. Moreover, numerical examples show that the designed low-resolution waveforms achieve lower estimation error and better resolvability than waveforms designed by competing algorithms in super-resolution DOA estimation.

在MIMO雷达系统中采用低分辨率（例如，1位）数模转换器（dac）可以在不显著影响性能的情况下大幅降低硬件成本和功耗，特别是对于大型天线阵列。为了提高多输入多输出（MIMO）雷达系统的目标定位性能，本文考虑了低分辨率波形的设计。我们采用多信号分类（MUSIC）得到的到达方向（DOA）的渐近均方误差（MSE）作为设计度量。在假设MIMO雷达接收阵列为标准均匀线性阵列（即单元间阵列间距等于波长的一半）的情况下，我们证明了设计度量具有紧凑的表达式，便于性能界的理论分析。为了设计可由低位dac生成的低分辨率波形，我们提出了基于块坐标下降法和Dinkelbach变换的两种迭代方法。我们证明了所提出的方法具有保证收敛性。数值算例表明，在超分辨率DOA估计中，所设计的低分辨率波形比竞争算法设计的波形具有更小的估计误差和更好的可分辨性。

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引用次数: 0

Reversible data hiding for color images based on a frequency-first partial assignment strategy 基于频率优先部分分配策略的彩色图像可逆数据隐藏

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-04 DOI: 10.1016/j.sigpro.2025.110429

Wei-Chun Lin , Tsai-Ju Lee , Hsin-Lung Wu

This paper investigates adaptive reversible data hiding (RDH) schemes for color images based on optimal 3D-mapping selection. A typical 3D-mapping selection scheme consists of two main steps. First, a histogram of 3D-prediction-error vectors is generated for a given color image using a pixel predictor. Then, a reversible 3D-mapping is determined through a selection mechanism. This study focuses primarily on the construction of the 3D-mapping selection mechanism. Previous approaches include iteratively modifying a basic 3D mapping, employing deep reinforcement learning to search for an optimal 3D mapping, and constructing a 3D mapping by optimizing 2D mappings. However, these state-of-the-art selection schemes often suffer from either high computational cost or limited search space. To enhance embedding performance, this paper proposes a novel 3D-mapping selection framework based on a frequency-first heuristic partial assignment strategy. In this strategy, 3D prediction error vectors with higher histogram frequencies are allocated a larger predefined mapping range. This method effectively reduces the search space and results in an efficient RDH algorithm. Comprehensive experimental analysis demonstrates that the proposed method outperforms existing adaptive histogram modification-based RDH approaches on most test color images.

研究了基于最优三维映射选择的彩色图像自适应可逆数据隐藏（RDH）方案。典型的3d贴图选择方案包括两个主要步骤。首先，使用像素预测器为给定的彩色图像生成3d预测误差向量的直方图。然后，通过选择机制确定可逆的3d映射。本研究主要围绕三维映射选择机制的构建展开。以前的方法包括迭代修改基本的3D映射，使用深度强化学习来搜索最优的3D映射，以及通过优化2D映射来构建3D映射。然而，这些最先进的选择方案往往存在计算成本高或搜索空间有限的问题。为了提高嵌入性能，本文提出了一种基于频率优先启发式部分分配策略的三维映射选择框架。在该策略中，直方图频率较高的3D预测误差向量被分配到更大的预定义映射范围。该方法有效地减小了搜索空间，得到了一种高效的RDH算法。综合实验分析表明，该方法在大多数测试彩色图像上优于现有的基于自适应直方图修改的RDH方法。

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引用次数: 0

New sufficient condition of ℓ1−βℓq method for robust signal recovery 新的1 - β q鲁棒信号恢复方法的充分条件

IF 3.6 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2026-05-01 Epub Date: 2025-12-07 DOI: 10.1016/j.sigpro.2025.110435

Jing Zhang , Wendong Wang

The problem of sparse signal recovery has drawn widespread attention from researchers. Based on high-order restricted isometry property (RIP) analysis, this paper establishes a new sufficient condition for

ℓ_{1} - β ℓ_{q}

method to guarantee robust signal recovery. This condition is shown to be better than the existing ones. Notably, when specialized to

ℓ_{1} - ℓ_{2}

method, our derived condition yields a better upper bound on RIC compared to the state-of-the-art ones. Furthermore, our analysis provides a theoretical perspective that extends the recovery guarantees of the convex ℓ₁ method to the non-convex

ℓ_{1} - β ℓ_{q}

method.

稀疏信号恢复问题引起了研究者的广泛关注。基于高阶受限等距特性（RIP）分析，建立了保证信号鲁棒恢复的一个新的充分条件。该条件优于现有条件。值得注意的是，当专门化到1−2方法时，我们推导的条件在RIC上得到了比现有条件更好的上界。此外，我们的分析提供了一个理论视角，将凸1方法的恢复保证扩展到非凸1 - β 1 q方法。

引用次数: 0