Signal Processing最新文献_第2页

A high-resolution learning imaging method for THz-SAR moving targets based on AF-RPCA-Net 基于AF-RPCA-Net的THz-SAR运动目标高分辨率学习成像方法

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-13 DOI: 10.1016/j.sigpro.2026.110499

Xiaoyu Qin, Bin Deng, Hongqiang Wang

Terahertz-Synthetic Aperture Radar (THz-SAR) offers high frame rates and high resolution, making it particularly suitable for remote sensing applications, like dynamic monitoring of moving targets. However, due to the non-ideal motion of the airborne platform and the non-cooperative motion of targets, this phenomenon causes more severe defocusing compared with microwave band SAR. Traditional SAR imaging methods, if directly applied to image THz-SAR moving targets, often suffer from poor quality and low efficiency. To address this issue, this article proposes a moving target non-parametric learning imaging method based on the Deep Unfolding Network (DUN) framework. Firstly, an autofocusing module is derived based on the maximum imaging contrast and embedded within the Alternating Direction Method of Multipliers (ADMM) iterative solution process to achieve accurate compensation of azimuthal motion errors. Then, we introduce the concept of Robust Principal Component Analysis (RPCA) to achieve sparse recovery imaging of moving targets. Finally, based on the ADMM iterative solution process, we establish an imaging network, named AF-RPCA-Net, efficiently achieving model-data jointly driven moving target background separation and imaging. The proposed method is validated to be effective and efficient through experimental results derived from both simulated and measured data.

太赫兹合成孔径雷达（THz-SAR）提供高帧率和高分辨率，使其特别适用于遥感应用，如动态监测移动目标。然而，由于机载平台的非理想运动和目标的非协同运动，与微波波段SAR相比，这种现象造成了更严重的离焦。传统的SAR成像方法如果直接应用于太赫兹SAR运动目标成像，往往存在质量差、效率低的问题。为了解决这一问题，本文提出了一种基于深度展开网络（DUN）框架的运动目标非参数学习成像方法。首先，建立了基于最大成像对比度的自动对焦模块，并将其嵌入到交替方向乘法器（ADMM）迭代求解过程中，实现了方位运动误差的精确补偿；然后，引入鲁棒主成分分析（RPCA）的概念，实现运动目标稀疏恢复成像。最后，基于ADMM迭代求解过程，建立了AF-RPCA-Net成像网络，有效实现模型-数据联合驱动的运动目标背景分离与成像。仿真和实测数据的实验结果验证了该方法的有效性和有效性。

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引用次数: 0

Block decision feedback equalization for OSDM in underwater acoustic communications 水声通信中OSDM的块决策反馈均衡

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2025-12-25 DOI: 10.1016/j.sigpro.2025.110461

Shengqian Ma, Jing Han, Yujie Wang, Lingling Zhang, Zhuoran Qi, Xiaodong Cui

Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that effectively bridges the gap between orthogonal frequency-division multiplexing and single-carrier frequency-domain equalization. However, the time-varying nature of underwater acoustic (UWA) channels leads to inter-vector interference (IVI) in OSDM transmissions. To mitigate the effects of IVI, the complex exponential basis expansion model (CE-BEM) is used to explicitly accommodate Doppler spreads caused by the temporal variations in UWA channels. Based on this, a block decision feedback equalization (BDFE) algorithm is proposed to improve the BER performance of OSDM systems. However, the CE-BEM may occasionally induce significant channel approximation error under certain conditions. To mitigate this limitation, we propose an enhanced BDFE (E-BDFE) algorithm that integrates the minimum band-approximation-error sum-of-exponentials window. Furthermore, by exploiting the unique structure of the OSDM channel matrix, both the BDFE and E-BDFE algorithms achieve computational complexity that is approximately linear in the block length. Simulation results indicate that both algorithms outperform their block linear equalization counterparts, with the E-BDFE achieving superior BER performance compared to the BDFE.

正交信分复用（OSDM）是一种很有前途的调制方案，它有效地弥补了正交频分复用和单载波频域均衡之间的差距。然而，水声（UWA）信道的时变特性导致了OSDM传输中的矢量间干扰（IVI）。为了减轻IVI的影响，使用复指数基展开模型（CE-BEM）来明确地适应UWA信道中由时间变化引起的多普勒扩频。在此基础上，提出了一种块决策反馈均衡（BDFE）算法来提高OSDM系统的误码率性能。然而，在某些条件下，CE-BEM偶尔会引起显著的信道近似误差。为了减轻这一限制，我们提出了一种增强的BDFE （E-BDFE）算法，该算法集成了最小带近似误差指数和窗口。此外，通过利用OSDM信道矩阵的独特结构，BDFE和E-BDFE算法都实现了在块长度上近似线性的计算复杂度。仿真结果表明，两种算法都优于块线性均衡算法，其中E-BDFE算法的误码率优于BDFE算法。

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引用次数: 0

Robust learning under label noise via logit-based filtering and ranking-aware relabeling 标签噪声下的鲁棒学习，通过基于逻辑的过滤和等级感知的重标注

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.sigpro.2026.110498

Fanglong Wu , Min Yang , Peng Cheng , Zhisheng You

Label noise poses a significant challenge in supervised learning tasks such as image classification and face recognition, often steering models away from their optimal learning trajectory. To reduce the adverse impact of noisy annotations while effectively leveraging available training data, we propose a robust learning framework that exploits logit space distributions for noise identification, ranking-guided relabeling of closed-set noise, and noise-aware optimization. The key insight behind our approach is that clean non-target samples and noisy target-class samples that have not yet been memorized by the network tend to exhibit similar logit distribution patterns. Based on this observation, we design adaptive, class-specific decision boundaries for blind noise detection. For closed-set noise, we compute the margin between the top two logits from non-target classes as a confidence score and incorporate historical ranking statistics. A pseudo-label is assigned when either the logit margin or the historical average rank of the top-1 class satisfies predefined criteria. Finally, clean and relabeled samples are trained with different regularization strengths to improve robustness. Extensive experiments on three synthetic and four real-world noisy datasets, covering image classification and face recognition tasks, demonstrate the effectiveness and generality of the proposed method.

标签噪声对图像分类和人脸识别等监督学习任务构成了重大挑战，通常会使模型偏离最佳学习轨迹。为了减少噪声注释的不利影响，同时有效地利用可用的训练数据，我们提出了一个鲁棒的学习框架，该框架利用logit空间分布进行噪声识别、封闭集噪声的排序引导重新标记和噪声感知优化。我们的方法背后的关键见解是，干净的非目标样本和尚未被网络记忆的噪声目标类样本倾向于表现出相似的logit分布模式。基于这一观察，我们设计了自适应的、类特定的决策边界用于盲噪声检测。对于闭集噪声，我们计算非目标类的前两个logits之间的余量作为置信度分数，并结合历史排名统计。当前1类的logit裕度或历史平均排名满足预定义的标准时，分配伪标签。最后，用不同的正则化强度训练干净和重新标记的样本，以提高鲁棒性。在包含图像分类和人脸识别任务的3个合成数据集和4个真实噪声数据集上进行了大量实验，证明了该方法的有效性和通用性。

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引用次数: 0

Robust beamforming for MIMO-RIS systems with hardware impairments 具有硬件缺陷的MIMO-RIS系统的鲁棒波束形成

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2025-12-25 DOI: 10.1016/j.sigpro.2025.110460

Kunze Wu , Zhengyi Zhang , Jingya Ren, Chenglin Wang, Shiyong Chen, Weiheng Jiang

This paper presents a robust beamforming framework for multiple-input multiple-output (MIMO) systems enhanced by reconfigurable intelligent surfaces (RIS), accounting for practical hardware impairments. The system model incorporates key non-idealities, including low-resolution analog-to-digital converters (ADCs) and hybrid radio frequency (RF) chains affected by distortion. The central objective is to minimize the user-side transmit power through joint optimization of the analog and digital combiners, the RIS reffection coefffcients, and the transmit power level itself. Due to the high dimensionality and inherent nonconvexity of the formulated problem, we employ an alternating optimization (AO) scheme to partition the variables and simplify the solution process. Fractional programming (FP) is applied to derive closed-form expressions for the auxiliary variables, while the digital combiner is obtained using the Lagrangian multiplier technique. To address the optimization of the analog combiner and RIS conffguration, we further introduce the penalty dual decomposition (PDD) method. Simulation results confirm that the proposed design significantly outperforms baseline methods in reducing transmit power, even in the presence of hardware degradation. Moreover, the proposed algorithm exhibits rapid convergence and scalability across varying system conffgurations.

本文提出了一种多输入多输出（MIMO）系统的鲁棒波束形成框架，该框架通过可重构智能曲面（RIS）增强，考虑到实际硬件缺陷。该系统模型包含了关键的非理想特性，包括低分辨率模数转换器（adc）和受失真影响的混合射频（RF）链。中心目标是通过联合优化模拟和数字合成器、RIS反射系数和发射功率水平本身来最小化用户侧发射功率。由于该问题的高维性和固有的非凸性，我们采用交替优化（AO）格式对变量进行划分，简化了求解过程。采用分数规划方法推导辅助变量的封闭表达式，采用拉格朗日乘法器技术得到数字组合。为了解决模拟合成器和RIS配置的优化问题，我们进一步引入了惩罚对偶分解（PDD）方法。仿真结果证实，即使在存在硬件退化的情况下，所提出的设计在降低发射功率方面也明显优于基线方法。此外，该算法在不同的系统配置中具有快速收敛和可扩展性。

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引用次数: 0

Activity-dependent resolution adjustment for radar-based human activity recognition 基于雷达的人类活动识别的活动相关分辨率调整

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2025-12-21 DOI: 10.1016/j.sigpro.2025.110456

Do-Hyun Park, Min-Wook Jeon, Hyoung-Nam Kim

The rising demand for detecting hazardous situations has led to increased interest in radar-based human activity recognition (HAR). Conventional radar-based HAR methods predominantly rely on micro-Doppler spectrograms for recognition tasks. However, conventional spectrograms employ a fixed resolution regardless of the varying characteristics of human activities, leading to limited representation of micro-Doppler signatures. To address this limitation, we propose a time-frequency domain representation method that adaptively adjusts the resolution based on activity characteristics. This approach adaptively adjusts the spectrogram resolution in a nonlinear manner, emphasizing frequency ranges that vary with activity intensity and are critical to capturing micro-Doppler signatures. We validate the proposed method by training deep learning-based HAR models on datasets generated using our adaptive representation. Experimental results demonstrate that models trained with our method achieve superior recognition accuracy compared to those trained with conventional methods.

探测危险情况的需求不断增长，导致人们对基于雷达的人类活动识别（HAR）的兴趣增加。传统的基于雷达的HAR方法主要依靠微多普勒谱图进行识别任务。然而，传统的频谱图采用固定的分辨率，而不考虑人类活动的不同特征，导致微多普勒特征的有限表示。为了解决这一限制，我们提出了一种基于活动特征自适应调整分辨率的时频域表示方法。该方法以非线性方式自适应调整频谱图分辨率，强调随活动强度变化的频率范围，对于捕获微多普勒特征至关重要。我们通过在使用自适应表示生成的数据集上训练基于深度学习的HAR模型来验证所提出的方法。实验结果表明，与传统方法训练的模型相比，用该方法训练的模型具有更高的识别精度。

引用次数: 0

An information geometry interpretation for approximate message passing 近似消息传递的信息几何解释

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2025-12-23 DOI: 10.1016/j.sigpro.2025.110462

Bingyan Liu , An-An Lu , Mingrui Fan , Jiyuan Yang , Xiqi Gao

In this paper, a novel information geometry (IG) framework to solve the standard linear regression problem with non-Gaussian a priori distribution is proposed. The proposed framework is also simpler than that in previous works when the a priori distribution becomes Gaussian. By applying the framework, a new information geometry approach (IGA) for the basis pursuit de-noising (BPDN) in standard linear regression is derived. Its convergence behavior is then analyzed. To establish the relation between the IGA and the approximate message passing (AMP) algorithm, the approximate information geometry approach (AIGA) for BPDN is derived from the IGA, and proved to be equivalent to the AMP algorithm. We also show how the algorithm derived from the IG framework relates to the generalized AMP (GAMP) and vector AMP (VAMP). These intrinsic results offer a new perspective for the AMP algorithm, and clues for understanding and improving stochastic reasoning methods.

本文提出了一种新的信息几何框架来解决非高斯先验分布的标准线性回归问题。当先验分布变为高斯分布时，所提出的框架也比以前的工作更简单。应用该框架，推导了一种新的用于标准线性回归中基追踪去噪的信息几何方法（IGA）。然后分析了其收敛性。为了建立IGA与近似消息传递（AMP）算法之间的关系，在IGA的基础上推导出BPDN的近似信息几何方法（AIGA），并证明其等价于AMP算法。我们还展示了从IG框架导出的算法如何与广义AMP （GAMP）和向量AMP （VAMP）相关。这些内在结果为AMP算法提供了新的视角，也为理解和改进随机推理方法提供了线索。

引用次数: 0

Adaptive regularization parameter adjustment for total variation denoising 全变差去噪的自适应正则化参数调整

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.sigpro.2026.110494

Donghao Lv , Tianshun Li , Peihong Yang , Chao Zhang , Jianjun Li

Total variation denoising has been extensively used in the restoration of piecewise constant signals, which are highly valued in numerous practical applications. However, existing approaches often struggle with the choice of regularization parameter, potentially leading to suboptimal denoising performance. To address this issue, this paper presents an adaptive regularization parameter adjustment mechanism and incorporates it with total variation denoising algorithm. An optimization strategy based on the solution of differential equation is designed to determine the regularization parameter, enabling it to converge toward an optimal value automatically. This strategy is then integrated into the total variation denoising framework to dynamically adjust the regularization parameter during the denoising process. Simulations and experimental results confirm that the proposed method significantly enhances the denoising efficiency for piecewise constant signals.

全变差去噪在分段常数信号的复原中得到了广泛的应用，在许多实际应用中得到了高度重视。然而，现有的方法经常在正则化参数的选择上遇到困难，这可能导致去噪性能不理想。针对这一问题，本文提出了一种自适应正则化参数调整机制，并将其与全变分去噪算法相结合。设计了一种基于微分方程解的优化策略来确定正则化参数，使正则化参数自动收敛到最优值。然后将该策略集成到全变分去噪框架中，在去噪过程中动态调整正则化参数。仿真和实验结果表明，该方法显著提高了对分段常数信号的去噪效率。

引用次数: 0

DEANet : Adaptive RGB-T salient object detection with two-dimensional entropy-guided dual-domain feature interaction 基于二维熵导双域特征交互的自适应RGB-T显著目标检测

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.sigpro.2026.110489

Zerui Zhu , Dongmei Liu , Huaxiang Zhang , Li Liu , Fengfei Jin

RGB-T salient object detection (RGB-T SOD) aims to accurately localize salient objects by integrating complementary cues from RGB and thermal images, yet existing methods often overlook critical frequency-domain information. Our frequency-domain analysis reveals modality inconsistencies in salient regions, highlighting the need for adaptive modality evaluation. To address this issue, we propose a two-dimensional information entropy-based weighting strategy that quantifies structural complexity and adaptively guides modality contribution. Building upon this strategy, we develop the Dual-Domain Entropy-Aware Network (DEANet), which incorporates a Progressive Dual-domain Fusion and Refinement (PDFR) design-a coherent two-stage progressive mechanism. Stage 1 performs entropy-guided spatial-frequency interaction to generate high-quality fused features, while Stage 2 leverages these fused features to enhance original modality representations and refine saliency through spatial-channel perception. This progressive dual-domain formulation enables robust multimodal fusion and more accurate saliency estimation under diverse imaging conditions. Extensive experiments on three public benchmarks demonstrate that DEANet consistently surpasses 17 state-of-the-art methods across multiple evaluation metrics.

RGB- t显著目标检测（RGB- t SOD）旨在通过整合来自RGB和热图像的互补线索来准确定位显著目标，但现有方法往往忽略了关键的频域信息。我们的频域分析揭示了显著区域的模态不一致，强调了自适应模态评估的必要性。为了解决这一问题，我们提出了一种基于二维信息熵的加权策略，该策略量化了结构复杂性并自适应地指导了模态的贡献。在此策略的基础上，我们开发了双域熵感知网络（DEANet），它结合了渐进式双域融合和细化（PDFR）设计-一种连贯的两阶段渐进机制。阶段1执行熵引导的空间频率交互以生成高质量的融合特征，而阶段2利用这些融合特征增强原始模态表征并通过空间通道感知改善显著性。这种渐进式双域公式能够实现鲁棒的多模态融合和在不同成像条件下更准确的显著性估计。在三个公共基准上进行的广泛实验表明，DEANet在多个评估指标上始终超过17种最先进的方法。

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引用次数: 0

Optimal design of stable allpass variable fractional delay filters using matrix-based algorithms 基于矩阵算法的稳定全通可变分数阶延迟滤波器优化设计

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.sigpro.2026.110496

Ruijie Zhao , Chunlu Lai

The optimal designs of allpass variable fractional delay (VFD) filters based on phase response approximation are investigated. The weighted least squares (WLS) design that allows for arbitrary nonnegative weighting functions is formulated in matrix form, and the optimality condition is then derived as a matrix equation. Two efficient algorithms that are derived from the conjugate gradient (CG) technique are proposed to solve the WLS problem. Subsequently, an iterative reweighted least squares (IRLS) algorithm is developed for the minimax design problem, which converts the original problem into a series of WLS subproblems and solves them successively using the proposed WLS algorithms. A transformation method using Chebyshev polynomials is presented to circumvent numerical problems in calculation. The filter coefficients are arranged as matrices, achieving significant computation and memory space savings. The associated computational complexity is evaluated. Moreover, by introducing a delay shift parameter in the desired response, design accuracy can be improved significantly. The stability of allpass VFD filters is analyzed, and stability conditions based on the delay shift parameter and phase error are established. Comparisons with existing methods are provided to show the efficiency and effectiveness of the proposed algorithms.

研究了基于相位响应近似的全通可变分数延迟（VFD）滤波器的优化设计。将允许任意非负权函数的加权最小二乘（WLS）设计以矩阵形式表述，并推导出最优性条件为矩阵方程。从共轭梯度（CG）技术出发，提出了两种求解WLS问题的有效算法。随后，针对极大极小设计问题，提出了一种迭代重加权最小二乘（IRLS）算法，该算法将原问题转化为一系列WLS子问题，并利用所提出的WLS算法依次求解。提出了一种利用切比雪夫多项式的变换方法，避免了计算中的数值问题。滤波器系数以矩阵形式排列，大大节省了计算和存储空间。计算相关的计算复杂度。此外，通过在期望响应中引入延迟移位参数，可以显著提高设计精度。分析了全通VFD滤波器的稳定性，建立了基于延时偏移参数和相位误差的稳定条件。通过与现有方法的比较，证明了所提算法的效率和有效性。

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引用次数: 0

A low-latency FIR filter design based on maximum correntropy criterion: Design and performance evaluation 基于最大熵准则的低延迟FIR滤波器设计：设计与性能评估

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

Signal Processing

Pub Date : 2026-06-01 Epub Date: 2026-01-14 DOI: 10.1016/j.sigpro.2026.110502

Yifei Wang , Kai-Li Yin , Xiaohong Yin , Chenggang Li , Lu Lu

Traditional Finite Impulse Response (FIR) structures suffer from high latency, making it difficult to operate efficiently at high frequencies. To address this issue, this paper presents a novel Non-Canonical FIR Maximum Correntropy Criterion (NCMCC) adaptive filtering algorithm. The non-canonical FIR structure optimizes the critical processing path latency by rearranging the delay units and reversing the weight coefficient sequence, thus enabling higher-frequency operation. By integrating the MCC algorithm, the proposed method enhances robustness against non-Gaussian and impulsive noise. A detailed theoretical analysis, including stochastic differential equation modeling and Lyapunov stability assessment, confirms the convergence and steady-state performance of the algorithm. Simulation results demonstrate that NCMCC outperforms conventional approaches such as Least Mean Square (LMS) algorithm, Least Mean p-th Power (LMP) algorithm and Sign Algorithm (SA) algorithm in terms of convergence speed, noise resilience, and steady-state error; Under various complex environments, the proposed algorithm demonstrates significantly improved performance compared to the MCC algorithm. These results establish NCMCC as an efficient and robust solution for real-time signal processing in complex and noisy environments.

传统的有限脉冲响应（FIR）结构存在高延迟，难以在高频率下有效运行。为了解决这个问题，本文提出了一种新的非正则FIR最大相关熵准则（NCMCC）自适应滤波算法。非正则FIR结构通过重新排列延迟单元和反转权重系数序列来优化关键处理路径延迟，从而实现更高频率的操作。通过集成MCC算法，增强了对非高斯噪声和脉冲噪声的鲁棒性。详细的理论分析，包括随机微分方程建模和Lyapunov稳定性评估，证实了该算法的收敛性和稳态性能。仿真结果表明，NCMCC在收敛速度、抗噪声能力和稳态误差方面优于传统的最小均方算法（LMS）、最小平均p次幂算法（LMP）和符号算法（SA）；在各种复杂环境下，与MCC算法相比，该算法的性能得到了显著提高。这些结果表明，NCMCC是复杂和噪声环境下实时信号处理的高效鲁棒解决方案。

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引用次数: 0