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SFIFusion: Semantic-frequency integration for task-driven infrared and visible image fusion 用于任务驱动的红外和可见光图像融合的语义频率集成

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

Signal Processing

Pub Date : 2025-11-27 DOI: 10.1016/j.sigpro.2025.110419

Wei Zhou , Lina Zuo , Yingyuan Wang , Dan Ma , Yuan Gao , Yugen Yi

Image fusion integrates complementary features from source images to enhance human and machine vision. Existing methods face two key limitations: (1) prioritizing visual quality over semantic representation, limiting downstream task performance, and (2) relying on spatial domain features, neglecting high-frequency details like textures and edges. To address these, we propose SFIFusion, a task-oriented network for semantic-frequency feature fusion, specifically for infrared and visible images. SFIFusion incorporates a Semantic Enhancement Block (SEB) for deep semantic feature extraction, aligned with visual details via DINOv2 to ensure semantic consistency. The enriched semantic features are subsequently incorporated back into the fusion process, ensuring that final fused image is both visually refined and semantically robust. It also introduces a Frequency Enhancement Block (FEB), using Fourier transform to decompose images into amplitude (texture/style) and phase (structural details), preserving amplitude for visual richness and combining phase for structural integrity. Experiments show SFIFusion outperforms current methods in visual quality, quantitative metrics, and downstream tasks like object detection and semantic segmentation, demonstrating its practical applicability in complex scenarios. The source code is available at https://github.com/Zzuouo/SFIFusion.

图像融合集成了源图像的互补特征，以增强人类和机器视觉。现有的方法面临两个关键的局限性：(1)优先考虑视觉质量而不是语义表示，限制了下游任务的性能；(2)依赖于空间域特征，忽略了纹理和边缘等高频细节。为了解决这些问题，我们提出了SFIFusion，这是一个面向任务的网络，用于语义频率特征融合，特别是针对红外和可见光图像。SFIFusion集成了语义增强块（SEB），用于深度语义特征提取，通过DINOv2与视觉细节对齐，以确保语义一致性。随后将丰富的语义特征合并回融合过程中，确保最终融合的图像在视觉上精致且在语义上鲁棒。它还引入了频率增强块（FEB），使用傅里叶变换将图像分解为幅度（纹理/风格）和相位（结构细节），保留幅度以获得视觉丰富性，结合相位以获得结构完整性。实验表明，SFIFusion在视觉质量、定量指标以及下游任务（如目标检测和语义分割）方面优于当前方法，证明了其在复杂场景中的实际适用性。源代码可从https://github.com/Zzuouo/SFIFusion获得。

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引用次数: 0

Locating close-range radar reflections measured using time-varying clock offsets 定位近距离雷达反射测量使用时变时钟偏移

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

Signal Processing

Pub Date : 2025-11-27 DOI: 10.1016/j.sigpro.2025.110418

Andreas Jansson, Andreas Jakobsson

Many forms of small-size radars suffer from minor clock oscillations due to crystalline impurities in their internal clocks, causing time-varying clock offsets between the transmitter and the receivers. This offset causes a bias and an increased variance in the positioning of close-range targets, limiting the achievable accuracy well beyond the expected estimation limitations. Furthermore, an approximative model ignoring the curvature of the impinging wavefront is generally also used to form computationally efficient estimators. Such approximations will further limit the achievable performance, especially for close-range targets. In this paper, we examine the effects of both these forms of performance degradations, deriving analytical bounds on the expected achievable performance due to both forms of model mismatches. We further introduce a computationally efficient and robust direction of arrival estimator that partly allows for these model mismatches, significantly improving the estimation performance for close-range reflectors as compared to traditional estimators.

许多形式的小尺寸雷达由于其内部时钟中的晶体杂质而遭受轻微的时钟振荡，导致发射器和接收器之间的时变时钟偏移。这种偏移导致近距离目标定位的偏差和增加的差异，限制了可实现的精度远远超出预期的估计限制。此外，通常还使用忽略碰撞波前曲率的近似模型来形成计算效率高的估计器。这种近似将进一步限制可实现的性能，特别是对于近距离目标。在本文中，我们研究了这两种形式的性能下降的影响，推导了由于两种形式的模型不匹配而导致的预期可实现性能的解析界。我们进一步引入了一种计算效率高且鲁棒的到达方向估计器，该估计器在一定程度上允许这些模型不匹配，与传统估计器相比，显著提高了近距离反射器的估计性能。

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 : 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

Deep learning approaches for channel length estimation in blind multichannel systems 盲多通道系统中信道长度估计的深度学习方法

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

Signal Processing

Pub Date : 2025-11-25 DOI: 10.1016/j.sigpro.2025.110416

Abdulmajid Lawal

Blind channel estimation and equalization techniques aim to estimate the channel or equalizer using only the received signal without any prior knowledge of the transmitted input signal. However, most of these techniques fail completely when the channel length is misspecified. Hence, channel length knowledge is critical in aiding blind techniques to accurately determine the channel and produce optimal results. This work proposes a novel deep learning approach to estimate channel lengths in blind single-input multiple-output (SIMO) systems. Utilizing the symmetric properties of the covariance matrix, two architectures, covariance regression network (CovRNet) and covariance classification network (CovCNet), are proposed for regression and classification tasks, respectively. Simulations demonstrate significant improvements in accuracy and generalization compared to traditional methods across various signal-to-noise (SNR) levels, highlighting the potential of deep learning for efficiently aiding channel length estimation in blind systems.

盲信道估计和均衡技术的目的是仅使用接收到的信号来估计信道或均衡器，而不需要事先知道发射的输入信号。然而，当信道长度指定错误时，大多数这些技术完全失败。因此，信道长度知识对于帮助盲技术准确确定信道并产生最佳结果至关重要。这项工作提出了一种新的深度学习方法来估计盲单输入多输出（SIMO）系统中的信道长度。利用协方差矩阵的对称特性，提出了协方差回归网络（CovRNet）和协方差分类网络（CovCNet）两种结构，分别用于回归和分类任务。仿真表明，与传统方法相比，在各种信噪比（SNR）水平上，深度学习在准确性和泛化方面有显著提高，突出了深度学习在盲系统中有效辅助信道长度估计的潜力。

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 : 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

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 : 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

A regular superpixel generation method based on continuous edges 一种基于连续边缘的规则超像素生成方法

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

Signal Processing

Pub Date : 2025-11-21 DOI: 10.1016/j.sigpro.2025.110412

Daipeng Yang , Bo Peng , Tingyu Zhao , Xiaofan Li

Regular superpixels are often preferred because they can be more easily applied to subsequent tasks. Existing superpixel boundaries are often highly irregular, frequently exhibiting jagged and rough edges. In the human visual system, the fourth visual cortex is typically considered the region responsible for image segmentation. Edge information from lower visual cortices is integrated in the fourth visual cortex and used to highlight the foreground and facilitate image segmentation. Inspired by this, we propose a regular superpixel generation method based on continuous edges. Our method first models orientation-selective neurons and use their orientation properties to thin and connect edges, resulting in continuous edge segments. These edge segments are then extended and reasonably partitioned by regular grid edges to form closed regions. Finally, we extract the closed regions and merge them to generate the desired number of superpixels. Experiments show that, compared to other superpixel generation methods, the superpixels obtained by our method are more regular. In smooth regions, our method produces square-shaped superpixels, while in non-smooth regions, the boundaries of our superpixels either closedly adhere to the object contours or align with the grid edges, effectively segmenting the object regions. The source code for our method is available at https://github.com/DaipengYang7/RSCE.

常规超像素通常是首选，因为它们可以更容易地应用于后续任务。现有的超像素边界通常非常不规则，经常表现出锯齿状和粗糙的边缘。在人类视觉系统中，第四视觉皮层通常被认为是负责图像分割的区域。来自下视觉皮层的边缘信息被整合到第四视觉皮层，用于突出前景和促进图像分割。受此启发，我们提出了一种基于连续边缘的规则超像素生成方法。我们的方法首先对定向选择神经元进行建模，并利用它们的定向属性来细化和连接边缘，从而得到连续的边缘段。然后将这些边段用规则的网格边进行扩展和合理分割，形成封闭区域。最后，我们提取封闭区域并合并它们以生成所需的超像素数量。实验表明，与其他超像素生成方法相比，本文方法得到的超像素更有规则性。在光滑区域，我们的方法产生方形的超像素，而在非光滑区域，我们的超像素的边界要么紧密地附着在物体轮廓上，要么与网格边缘对齐，有效地分割了物体区域。我们的方法的源代码可从https://github.com/DaipengYang7/RSCE获得。

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引用次数: 0

Inter-array beam cross-correlation for spatially close multipath discrimination in the reliable acoustic path 可靠声程中空间近距离多径识别的阵列间波束互相关

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

Signal Processing

Pub Date : 2025-11-17 DOI: 10.1016/j.sigpro.2025.110411

Ning Wang , Rui Duan , Zhanchao Liu , Xiaoyi Zhou

The directions of arrival (DOA) of the direct path (D) and the surface-reflected path (SR) from a submerged moving source to deep receivers provide range and depth information for source localization in Reliable Acoustic Path (RAP) environments. However, the grazing angles of the D and SR paths are typically very close, posing significant challenges for accurate discrimination. This study proposes a high-resolution multipath DOA estimation method based on horizontally distributed vertical line arrays (VLAs) in the deep sea. This method applies inter-array beam cross-correlation in the time delay–Doppler shift domain, thereby distinguishing the multipath arrivals in this two-dimensional domain rather than the one-dimensional domain of angle. The process also benefits from the spatial gain of beamforming and the temporal (pulse compression) gain of inter-beam cross-correlation, yielding two key advantages: (1) substantial enhancement of the signal-to-noise ratio (SNR), facilitating the reliable extraction of weak multipath arrivals, particularly the SR; and (2) improved spatial filtering, which effectively suppresses uncorrelated interference between arrays. Experimental results from the South China Sea demonstrate that the proposed method accurately extracts the DOAs of both the D and SR paths of a moving source at a depth of approximately 72 meters, even under significant surface ship interference. In contrast, conventional single-array high-resolution methods, such as compressive sensing and MVDR, fail to extract the SR path.

在可靠声路径（RAP）环境中，水下移动源到深部接收器的直接路径(D)和表面反射路径（SR）的到达方向（DOA）为源定位提供了范围和深度信息。然而，D和SR路径的掠角通常非常接近，这对准确识别构成了重大挑战。提出了一种基于水平分布垂直线阵列（VLAs）的深海高分辨率多径DOA估计方法。该方法在时延-多普勒频移域应用阵列间波束互相关，从而在该二维域而不是一维角度域中区分多径到达。该过程还受益于波束形成的空间增益和波束间互相关的时间（脉冲压缩）增益，产生两个关键优势：(1)显著提高了信噪比（SNR），有助于可靠地提取弱多径到达，特别是SR；(2)改进空间滤波，有效抑制阵列间不相关干扰。南海实验结果表明，该方法在水面舰艇明显干扰的情况下，仍能准确提取深度约为72米的动源D路径和SR路径的doa。相比之下，传统的单阵列高分辨率方法，如压缩感知和MVDR，无法提取SR路径。

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引用次数: 0

A space-time blind adaptive anti-interference method for distributed GNSS antenna arrays 分布式GNSS天线阵的空时盲自适应抗干扰方法

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

Signal Processing

Pub Date : 2025-11-13 DOI: 10.1016/j.sigpro.2025.110409

Binbin Ren , Song Li , Feiqiang Chen , Chenxuan Liu , Shaojie Ni

Antenna arrays and spatial adaptive processing are among the most effective means to enhance the anti-interference capability of Global Navigation Satellite System (GNSS) receivers. This paper proposes a novel space-time blind adaptive anti-interference method for a new spatially distributed GNSS antenna array. Compared to conventional spatial-only processing, space-time processing compensates for baseband delays caused by large inter-element spacing, achieving superior interference suppression. However, traditional blind algorithms often attenuate satellite signals due to the lack of gain constraints, reducing tracking and measurement precision. To address this, the principle of noncoherent channel combination is extended to space-time array processing to improve GNSS tracking precision. Since the original two-stage framework was designed for compact arrays and spatial-only processing, it does not address the code phase inconsistencies introduced by space-time processing in distributed arrays. A channel selection stage is thus introduced. The framework comprises three stages: interference suppression, optimal channel selection, and noncoherent channel combination. The method retains the advantages of blind processing and requires no external auxiliary information. Comparative analysis with state-of-the-art space-time blind anti-interference approaches demonstrates its effectiveness. Simulations confirm superior tracking performance and interference mitigation, validating its advantages over existing techniques.

天线阵列和空间自适应处理是提高全球导航卫星系统（GNSS）接收机抗干扰能力的最有效手段之一。针对一种新型空间分布式GNSS天线阵列，提出了一种新的空时盲自适应抗干扰方法。与传统的纯空间处理相比，时空处理补偿了由于元间间距大造成的基带延迟，实现了更好的干扰抑制。然而，传统的盲算法由于缺乏增益约束，往往使卫星信号衰减，降低了跟踪和测量精度。为解决这一问题，将非相干信道组合原理扩展到空时阵列处理中，提高GNSS跟踪精度。由于最初的两阶段框架是为紧凑数组和仅空间处理而设计的，因此它没有解决分布式数组中由时空处理引入的代码相位不一致性。这样就引入了信道选择阶段。该框架包括三个阶段：干扰抑制、最优信道选择和非相干信道组合。该方法保留了盲处理的优点，不需要外部辅助信息。通过与现有时空盲抗干扰方法的对比分析，验证了该方法的有效性。仿真验证了其优越的跟踪性能和抗干扰能力，验证了其优于现有技术的优势。

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引用次数: 0

A structure adaptivity variation-based segmentation model for image with retinex and noise 基于结构自适应变化的视网膜和噪声图像分割模型

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

Signal Processing

Pub Date : 2025-11-13 DOI: 10.1016/j.sigpro.2025.110410

Yuan Wang , Tieyong Zeng , Zhi-Feng Pang , Hong Ge

Images often suffer from intensity inhomogeneity and noise during the imaging process, which poses considerable challenges for image segmentation. This paper proposes a novel adaptive structure variational model integrating Retinex theory for image segmentation, which effectively addresses intensity inhomogeneity and noise in the segmentation process. Grounded in Retinex theory, we decompose the image and impose two constraints: a piecewise-constant constraint on the reflectance to delineate homogeneous regions amid inhomogeneity, and a spatial smoothness constraint on the illumination to model the bias field. The primary novelty of the work lies in the introduction of an adaptive weighted matrix, comprising a rotation matrix and a scaling matrix, coupled with the gradient operator. This design enables the proposed model to perform anisotropic regularization, allowing it to adaptively capture directional structural features, preserve complex boundaries and textures, and simultaneously suppress noise effectively. We establish the existence of a solution for the proposed model and employ an efficient alternating minimization algorithm for numerical solution. Numerical experiments on synthetic, natural, and medical images demonstrate the desirable performance of the proposed model.

图像在成像过程中往往存在强度不均匀性和噪声，这对图像分割提出了相当大的挑战。结合Retinex理论，提出一种新的自适应结构变分模型用于图像分割，有效地解决了图像分割过程中的强度不均匀性和噪声问题。基于Retinex理论，我们对图像进行分解，并施加两个约束：对反射率进行分段常数约束，以在非均匀性中描绘均匀区域；对光照进行空间平滑约束，以模拟偏置场。这项工作的主要新颖之处在于引入了一个自适应加权矩阵，包括旋转矩阵和缩放矩阵，再加上梯度算子。该设计使所提出的模型能够进行各向异性正则化，使其能够自适应捕获定向结构特征，保留复杂的边界和纹理，同时有效地抑制噪声。我们建立了该模型解的存在性，并采用了一种高效的交替极小化算法求解数值解。在合成图像、自然图像和医学图像上的数值实验证明了该模型的良好性能。

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引用次数: 0