Digital Signal Processing最新文献_第5页

Multi-component LFM signal representation method under impulsive noise: Principle, method and application 脉冲噪声下多分量LFM信号表示方法：原理、方法及应用

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-02 DOI: 10.1016/j.dsp.2025.105868

Weiwei Shang , Yong Guo , Lidong Yang

In the presence of impulse noise modeled by the α-stable distribution, conventional noise suppression methods inevitably introduce cross-terms when processing multi-component signal, leading to significant deviations in subsequent signal representation and parameter estimation. To effectively address this issue, this paper develops an impulsive noise suppression technique based on K-medoids cluster (KMC), and proposes two representation methods for multi-component linear frequency modulation (LFM) signal under impulse noise. Firstly, the reason for cross-terms introduction is analyzed from the mathematical perspective, and subsequently a KMC-based impulsive noise suppression technology is developed. Secondly, KMC-fractional Fourier transform (KMC-FRFT) and KMC-synchrosqueezing transform (KMC-SST) are proposed, enabling precise characterization of multi-component LFM signal in the fractional domain and time-frequency domain, respectively. Finally, KMC-FRFT is applied to the parameter estimation of multi-component LFM signal under impulsive noise. Simulation experiments demonstrate that, from fractional domain and time-frequency domain, KMC not only suppresses high-amplitude burst impulsive noise, but also completely resolves the cross-terms problem inherent in existing methods. On this basis, under impulsive noise, KMC-FRFT and KMC-SST effectively capture the fractional spectral characteristic and time-frequency distribution characteristic of multi-component LFM signal from complementary perspectives. For both simulated and measured impulsive noise, RMSE demonstrates that KMC-FRFT can accurately estimate the parameters of weak component signal when GSNR  ≥  6dB, addressing the issue of incorrect parameter estimation caused by the cross-terms interference.

在α-稳定分布建模的脉冲噪声存在的情况下，传统的噪声抑制方法在处理多分量信号时不可避免地引入交叉项，导致后续的信号表示和参数估计出现较大偏差。为了有效地解决这一问题，本文发展了一种基于k -媒质聚类（KMC）的脉冲噪声抑制技术，并提出了两种多分量线性调频（LFM）信号在脉冲噪声下的表示方法。首先从数学的角度分析了交叉项引入的原因，然后提出了一种基于kmc的脉冲噪声抑制技术。其次，提出了kmc -分数阶傅里叶变换（KMC-FRFT）和kmc -同步压缩变换（KMC-SST），分别在分数域和时频域对多分量LFM信号进行精确表征。最后，将KMC-FRFT应用于脉冲噪声下多分量LFM信号的参数估计。仿真实验表明，从分数域和时频域两方面来看，KMC不仅能够抑制高幅值突发脉冲噪声，而且完全解决了现有方法固有的交叉项问题。在此基础上，在脉冲噪声下，KMC-FRFT和KMC-SST从互补的角度有效捕获了多分量LFM信号的分数阶谱特征和时频分布特征。对于模拟和测量的脉冲噪声，RMSE均表明，当GSNR ≥ 6dB时，KMC-FRFT可以准确估计弱分量信号的参数，解决了交叉项干扰导致的参数估计错误的问题。

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引用次数: 0

Parameter measurement and sub-Nyquist sampling of Gaussian-like pulse streams with non-ideal filter 非理想滤波器类高斯脉冲流的参数测量与亚奈奎斯特采样

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.dsp.2026.105899

Guoxing Huang, Shumei Lv, Hong Peng, Jingwen Wang, Yu Zhang

Gaussian-like pulse sequences play a vital role in communication and signal processing. The recently developed finite rate of innovation (FRI) theory offers an effective technical means for sub-Nyquist measurement of Gaussian-like pulse streams. However, current measurement methods are restricted to signals with consistent waveforms and overlook non-idealities in hardware filters. In this paper, a parameter measurement method for sub-Nyquist sampling of Gaussian-like pulse streams with non-ideal filtering is proposed. First, drawing upon the concept of shift-invariant (SI) subspace, irregular pulses are modeled as combinations of known waveforms, so that the signal can be characterized using a finite number of parameters. It solves the problem of parameter measurement under the condition of waveform inconsistency. Second, based on the idea of spectrum shifting, a dual-channel sampling structure based on constrained random modulation is proposed. The signal’s high-energy spectral content is modulated to the baseband, which facilitates the low-pass filtering and low-speed sampling of the signal. Finally, a joint parameter measurement and reconstruction algorithm is developed, which effectively counteracts non-ideal impacts and achieves high-precision signal reconstruction. The effectiveness of the proposed approach in accurately measuring signal parameters has been validated through simulations and hardware testing.

类高斯脉冲序列在通信和信号处理中起着至关重要的作用。近年来发展起来的有限创新率理论为类高斯脉冲流的亚奈奎斯特测量提供了一种有效的技术手段。然而，目前的测量方法仅限于具有一致波形的信号，而忽略了硬件滤波器的非理想性。提出了一种非理想滤波类高斯脉冲流亚奈奎斯特采样的参数测量方法。首先，利用移不变（SI）子空间的概念，将不规则脉冲建模为已知波形的组合，从而可以使用有限数量的参数来表征信号。解决了波形不一致情况下的参数测量问题。其次，基于频谱移位的思想，提出了一种基于约束随机调制的双通道采样结构。将信号的高能频谱内容调制到基带，便于对信号进行低通滤波和低速采样。最后，提出了一种联合参数测量与重构算法，有效地抵消了非理想影响，实现了高精度的信号重构。通过仿真和硬件测试，验证了该方法在精确测量信号参数方面的有效性。

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引用次数: 0

GPF-GAN: An unsupervised generative adversarial network for joint gradient and pixel-constrained fusion of infrared and visible images GPF-GAN：用于红外和可见光图像联合梯度和像素约束融合的无监督生成对抗网络

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.dsp.2026.105902

Pengpeng Xie, Ziyang Ding, Qianfan Li, Cong Shi, Shibo Bin

Current image fusion algorithms often face modality preference issues: they either excessively depend on the thermal radiation features of infrared images, leading to the loss of visible light texture details, or they prioritize visible light images, which undermines infrared target detection. This makes it challenging to achieve a dynamic balance and collaborative optimization of information from both modalities in complex scenarios. This asymmetric fusion approach makes it difficult for the system to simultaneously preserve sensitivity to thermal radiation targets while maintaining the ability to resolve texture details under extreme lighting conditions. To address this, the paper proposes an infrared and visible light fusion model that incorporates a gradient-pixel joint constraint. Our approach eliminates the complexity and uncertainty associated with manual feature extraction, while effectively leveraging shallow features through multiple shortcut connections. Within the framework of Generative Adversarial Networks, we design a gradient-pixel joint loss function that strikes a balance between preserving significant targets in the infrared image and maintaining the texture structure in the visible light image, thereby enhancing image detail and retaining high-contrast information. To thoroughly evaluate the performance of the proposed method, we conducted systematic experiments using the TNO and RoadScene benchmark datasets, comparing it with eleven state-of-the-art fusion algorithms. The experimental results demonstrate that the proposed method offers significant advantages in both subjective visual quality and objective evaluation metrics. In terms of qualitative evaluation, the fusion results not only preserve natural lighting transitions but, more importantly, accentuate thermal radiation targets in the infrared image while fully retaining the texture details of the visible light image. Quantitative analysis reveals that the proposed method significantly improves metrics such as Mutual Information (MI) and Spatial Frequency (SF). This provides new insights in the field of multimodal image fusion and contributes to balancing the complementary advantages of different modality features.

目前的图像融合算法往往面临模态偏好问题：要么过度依赖红外图像的热辐射特征，导致丢失可见光纹理细节，要么优先考虑可见光图像，从而破坏红外目标检测。这使得在复杂场景中实现两种模式信息的动态平衡和协作优化具有挑战性。这种不对称融合方法使得系统难以同时保持对热辐射目标的敏感性，同时保持在极端光照条件下解决纹理细节的能力。为了解决这一问题，本文提出了一种包含梯度-像素联合约束的红外和可见光融合模型。我们的方法消除了人工特征提取的复杂性和不确定性，同时通过多个快捷连接有效地利用了浅层特征。在生成对抗网络的框架内，我们设计了一个梯度-像素联合损失函数，在保留红外图像中的重要目标和保留可见光图像中的纹理结构之间取得平衡，从而增强图像细节并保留高对比度信息。为了全面评估该方法的性能，我们使用TNO和RoadScene基准数据集进行了系统实验，并将其与11种最先进的融合算法进行了比较。实验结果表明，该方法在主观视觉质量和客观评价指标上都具有显著的优势。在定性评价方面，融合结果不仅保留了自然光照过渡，更重要的是在充分保留可见光图像纹理细节的同时，突出了红外图像中的热辐射目标。定量分析表明，该方法显著提高了互信息（MI）和空间频率（SF）等指标。这为多模态图像融合领域提供了新的见解，有助于平衡不同模态特征的互补优势。

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引用次数: 0

Robust time-frequency preamble detection for LoRa-modulated signals using optimized generalized likelihood ratio test 基于优化广义似然比检验的lora调制信号鲁棒时频前导检测

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.dsp.2026.105892

Nan Chen, Xia Liu, Huafeng Wu

This paper proposes a robust preamble detection algorithm for long range radio signals. The method integrates the short-time Fourier transform (STFT) with a generalized likelihood ratio test (GLRT), detecting signals via coherent integration along an estimated time-frequency trajectory. First, a binary hypothesis testing framework is established based on the time-frequency characteristics of the LoRa preamble to discriminate between signal and noise. Then, an STFT with optimized window parameters is adopted to extract time-frequency features. To address the uncertainty of the preamble’s starting position, a discrete time-frequency path model is introduced. By exploiting the known linear frequency modulation structure and optimized window parameters, a discretized grid path is constructed in the time-frequency domain to estimate the signal trajectory. Sliding coherent accumulation is then performed along these paths to form the GLRT statistic. Theoretical analysis shows that the STFT coefficients follow a chi-square distribution under noise-only conditions and a non-central chi-square distribution in the presence of a signal. Based on this, the probability distributions of the coherent accumulated value and the test statistic are derived. Finally, an adaptive threshold computation method is also proposed to optimally balance the detection probability and false alarm rate. Simulations are conducted under various spreading factors, preamble lengths, and carrier frequency offsets. Results indicate that the proposed GLRT detector improves detection probability by about 25% and synchronization accuracy by about 17% in low-SNR scenarios, compared with conventional methods.

提出了一种鲁棒的远程无线电信号前导检测算法。该方法将短时傅里叶变换（STFT）与广义似然比检验（GLRT）相结合，沿估计的时频轨迹通过相干积分检测信号。首先，基于LoRa前导的时频特性，建立二元假设检验框架，区分信号和噪声；然后，采用优化窗口参数的STFT提取时频特征；为了解决前导起始位置的不确定性，引入了离散时频路径模型。利用已知的线性调频结构和优化的窗口参数，在时频域构造离散网格路径来估计信号轨迹。然后沿着这些路径进行滑动相干累积，形成GLRT统计量。理论分析表明，STFT系数在无噪声条件下服从卡方分布，在有信号时服从非中心卡方分布。在此基础上，推导了相干累积值的概率分布和检验统计量。最后，提出了一种自适应阈值计算方法，以最优平衡检测概率和虚警率。在不同的扩频因子、前导长度和载波频偏下进行了仿真。结果表明，在低信噪比情况下，与传统方法相比，所提出的GLRT检测器的检测概率提高了约25%，同步精度提高了约17%。

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引用次数: 0

Research on denoising of rolling bearing vibration signals based on the ISSA-VMD-JWTD method 基于ISSA-VMD-JWTD方法的滚动轴承振动信号去噪研究

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.dsp.2026.105912

Xuchao Bai , Yuxian Wang , Chunfang Zhang

This paper proposes a novel method based on an improved sparrow search algorithm, variational mode decomposition (VMD), and joint wavelet threshold denoising (ISSA-VMD-JWTD). During the optimization process, cubic mapping is utilized to initialize the sparrow population, while weighted kurtosis serves as the fitness function to optimize the critical parameters of Variational Mode Decomposition (VMD). In the decomposition and denoising procedure, the vibration signal is first decomposed using VMD with the optimal parameter combination, followed by adaptive selection of effective components to eliminate low-frequency noise. Subsequently, a multi-objective optimization mechanism is established to autonomously determine the optimal wavelet threshold parameters for different wavelet families (including dbN, symN, and coifN). Parallel secondary denoising is then performed on the reconstructed ISSA-VMD signal using the optimized parameters from each wavelet family. Finally, the denoising results from multiple wavelet families are integrated through a variance-based weighting strategy to produce the ultimate denoised signal. Experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio of denoised signals compared to other denoising approaches, while exhibiting superior adaptability and robustness for rolling bearing vibration signals under diverse operating conditions.

本文提出了一种基于改进的麻雀搜索算法、变分模态分解（VMD）和联合小波阈值去噪（ISSA-VMD-JWTD）的新方法。在优化过程中，利用三次映射初始化麻雀种群，加权峰度作为适应度函数对变分模态分解（VMD）的关键参数进行优化。在分解去噪过程中，首先利用最优参数组合对振动信号进行VMD分解，然后自适应选择有效分量去除低频噪声。随后，建立多目标优化机制，自主确定不同小波族（包括dbN、symN和coifN）的最优小波阈值参数。然后利用每个小波族的优化参数对重构的ISSA-VMD信号进行并行二次去噪。最后，通过基于方差的加权策略对多个小波族的去噪结果进行综合，得到最终去噪信号。实验结果表明，与其他去噪方法相比，该方法显著提高了去噪信号的信噪比，同时对不同工况下的滚动轴承振动信号具有较好的适应性和鲁棒性。

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引用次数: 0

Generalized low-rank matrix completion model with overlapping group error representation 具有重叠组误差表示的广义低秩矩阵补全模型

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-17 DOI: 10.1016/j.dsp.2026.105925

Wenjing Lu , Zhuang Fang , Liang Wu , Liming Tang , Hanxin Liu , Chuanjiang He

The low-rank matrix completion (LRMC) technology has achieved remarkable results in low-level visual tasks. There is an underlying assumption that the real-world matrix data is low-rank in LRMC. However, when matrix data do not strictly satisfy the low-rank property, this assumption creates serious challenges for existing matrix recovery methods. Fortunately, there exist feasible schemes that devise appropriate and effective priori representations for describing the intrinsic information of real data. In this paper, we first model the matrix data Y as the sum of a low-rank approximation component

X

and an approximation error component

E

. This finer-grained data decomposition framework allows each component of information to be portrayed more precisely. To effectively characterize the structured error, we design an overlapping group error representation (OGER) function, which captures structured sparsity by modeling locally correlated errors. Finally, we develop an efficient optimization algorithm based on the alternating direction method of multipliers (ADMM), which integrates the majorization-minimization (MM) technique to ensure efficient convergence. We also provide a rigorous theoretical analysis, including a detailed proof of the convexity of the OGER function and the convergence guarantees of our algorithm. In addition, numerical experiment results demonstrate that the proposed model consistently outperforms existing competing models.

低秩矩阵补全（LRMC）技术在低阶视觉任务中取得了显著的效果。有一个潜在的假设，即现实世界的矩阵数据在LRMC中是低秩的。然而，当矩阵数据不严格满足低秩性时，这一假设对现有的矩阵恢复方法提出了严峻的挑战。幸运的是，有可行的方案，设计适当和有效的先验表示来描述真实数据的内在信息。在本文中，我们首先将矩阵数据Y建模为低秩近似分量X和近似误差分量e的和，这种更细粒度的数据分解框架允许更精确地描绘信息的每个分量。为了有效地表征结构误差，我们设计了一个重叠组误差表示（OGER）函数，该函数通过对局部相关误差建模来捕获结构稀疏性。最后，我们提出了一种基于乘法器交替方向法（ADMM）的高效优化算法，该算法集成了极大化-极小化（MM）技术以保证算法的高效收敛。我们还提供了严格的理论分析，包括OGER函数的凸性的详细证明和我们的算法的收敛性保证。此外，数值实验结果表明，该模型优于现有的竞争模型。

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引用次数: 0

Blind seismic denoising via ensemble iterative data refinement with adaptive spectral-spatial feature fusion 基于自适应光谱空间特征融合的集成迭代数据盲去噪方法

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-19 DOI: 10.1016/j.dsp.2026.105936

Zhanzhan Shi , Guo Huang , Huailai Zhou , Su Pang , Yuanjun Wang

Attenuating random noise without access to clean training targets or precise noise models remains a significant challenge for seismic data processing. This paper proposes the ensemble iterative data refinement (EIDR) framework for robust blind noise suppression, featuring three methodological innovations: 1) J-invariant ensemble learning, which integrates intermediate denoised estimations with raw noisy inputs to enable training free from explicit noise distribution assumptions, thereby eliminating reliance on predefined noise models; 2) Trainable fractional Fourier transform (FrFT) embedding layers that replace conventional convolutional blocks, facilitating adaptive frequency-spatial feature fusion through learnable fractional orders; and 3) A structure-preserving U-shape architecture (ULite) utilizing dual-stream discrete wavelet transform (DWT) pooling to preserve critical high-frequency microstructural information during downsampling. EIDR was rigorously evaluated on synthetic Marmousi data contaminated with non-stationary noise and the Mobil AVO Viking Graben field dataset. On synthetic data, EIDR achieved an output SNR of 42.755 dB, surpassing state-of-the-art (SOTA) self-supervised benchmarks by up to 17.965 dB and closing 92.2% of the performance gap compared to fully supervised models. Field validation confirmed that EIDR effectively suppresses complex unknown noise while preserving structural fidelity and amplitude integrity. The framework demonstrates significant practical feasibility, achieving a processing speed of 0.193 ms per 64 × 64 patch on an NVIDIA RTX 3090 GPU. These results establish EIDR as a highly effective and practical solution for blind seismic denoising under realistic constraints.

在没有清晰的训练目标或精确的噪声模型的情况下，如何衰减随机噪声仍然是地震数据处理的一个重大挑战。本文提出了用于鲁棒盲噪声抑制的集成迭代数据细化（EIDR）框架，该框架具有三个方法创新：1)j不变集成学习，将中间去噪估计与原始噪声输入集成在一起，使训练不需要明确的噪声分布假设，从而消除对预定义噪声模型的依赖；2)可训练分数阶傅里叶变换（FrFT）嵌入层取代传统的卷积块，通过可学习的分数阶促进自适应频率-空间特征融合；3)利用双流离散小波变换（DWT）池化的u型结构（ULite），在下采样过程中保留关键的高频微观结构信息。在含有非平稳噪声的Marmousi合成数据和Mobil AVO Viking地堑油田数据集上，对EIDR进行了严格的评估。在合成数据上，EIDR实现了42.755 dB的输出信噪比，比最先进的（SOTA）自监督基准高出17.965 dB，与完全监督模型相比，缩小了92.2%的性能差距。现场验证证实，EIDR有效地抑制了复杂的未知噪声，同时保持了结构保真度和幅度完整性。该框架具有显著的实际可行性，在NVIDIA RTX 3090 GPU上实现了每个64 × 64补丁0.193 ms的处理速度。这些结果表明，EIDR是在现实约束条件下有效和实用的盲地震去噪解决方案。

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引用次数: 0

Correct estimation of higher-order spectra: From theoretical challenges to practical multi-channel implementation in SignalSnap 高阶频谱的正确估计：从理论挑战到SignalSnap的实际多通道实现

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.dsp.2026.105893

Markus Sifft, Armin Ghorbanietemad, Fabian Wagner, Daniel Hägele

Higher-order spectra (Brillinger’s polyspectra) offer powerful methods for solving critical problems in signal processing and data analysis. Despite their significant potential, their practical use has remained limited due to unresolved mathematical issues in spectral estimation, including the absence of unbiased and consistent estimators and the high computational cost associated with evaluating multidimensional spectra. Consequently, existing tools frequently produce artifacts-no existing software library correctly implements Brillinger’s cumulant-based trispectrum-or fail to scale effectively to real-world data volumes, leaving crucial applications like multi-detector spectral analysis largely unexplored.

In this paper, we revisit higher-order spectra from a modern perspective, addressing the root causes of their historical underuse. We reformulate higher-order spectral estimation using recently derived multivariate k-statistics, yielding unbiased and consistent estimators that eliminate spurious artifacts and precisely align with Brillinger’s theoretical definitions. Our methodology covers single- and multi-channel spectral analysis up to the bispectrum (third order) and trispectrum (fourth order), enabling robust investigations of inter-frequency coupling, non-Gaussian behavior, and time-reversal symmetry breaking. Additionally, we introduce quasi-polyspectra to uncover non-stationary, time-dependent higher-order features. We implement these new estimators in SignalSnap, an open-source GPU-accelerated library capable of efficiently analyzing datasets exceeding hundreds of gigabytes within minutes.

In applications such as continuous quantum measurements, SignalSnap’s rigorous estimators enable precise quantitative matching between experimental data and theoretical models. With detailed derivations and illustrative examples, this work provides the theoretical and computational foundation necessary for establishing higher-order spectra as a reliable, standard tool in modern signal analysis.

高阶谱（布里林格多谱）为解决信号处理和数据分析中的关键问题提供了强有力的方法。尽管它们具有巨大的潜力，但由于光谱估计中未解决的数学问题，包括缺乏无偏和一致的估计器以及与评估多维光谱相关的高计算成本，它们的实际应用仍然有限。因此，现有的工具经常产生工件——没有现有的软件库正确地实现Brillinger的基于累积量的三光谱——或者不能有效地扩展到现实世界的数据量，使得像多探测器光谱分析这样的关键应用在很大程度上没有被探索。在本文中，我们从现代的角度重新审视高阶光谱，解决其历史上未充分利用的根本原因。我们使用最近导出的多元k统计量重新制定高阶光谱估计，产生无偏和一致的估计，消除了虚假的工件，并精确地与Brillinger的理论定义对齐。我们的方法涵盖单通道和多通道频谱分析，直至双频谱（三阶）和三频谱（四阶），能够对频间耦合，非高斯行为和时间反转对称性破断进行稳健的研究。此外，我们引入了准多光谱来揭示非平稳的、时变的高阶特征。我们在SignalSnap中实现了这些新的估计器，SignalSnap是一个开源的gpu加速库，能够在几分钟内有效地分析超过数百gb的数据集。在连续量子测量等应用中，SignalSnap的严格估计器可以实现实验数据和理论模型之间的精确定量匹配。通过详细的推导和举例说明，本工作为建立高阶谱作为现代信号分析中可靠的标准工具提供了必要的理论和计算基础。

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引用次数: 0

A multi-stage path aggregation module for small object detection on drone-captured scenarios 用于无人机捕获场景下小目标检测的多阶段路径聚合模块

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.dsp.2026.105901

Wenyuan Fan , Xuemei Xu , Zhaohui Jiang , Zehan Zhu

Small object detection remains a critical challenge due to limited pixel representation and uneven spatial distribution. In the absence of sufficient contextual information, it is difficult to extract discriminative and complete features for accurate detection. By analyzing multi-scale feature fusion within modern detectors, we proposed a Multi-stage Path Aggregation module(MPAM) composed of the Parallel Residual Fusion Module(PRFM) and the Differential Path Channel Aggregation Module(DPCAM). Through decomposing the path aggregation operation into multiple stages, MPAM significantly enhanced the feature maps’ capacity to accommodate and process contextual information. PRFM captured texture and semantic information from the multi-scale feature maps through skip connections. Moreover, a channel branch was added to enable the dynamic distribution of attention weights across both the channel and spatial dimensions. DPCAM is proposed to balance channel and spatial information from different feature maps through channel expansion operation. Additionally, Deep-wise Partial Attention(DPA) is designed to enhance the ability of representing features for small objects within complex backgrounds by balancing weights between local and global information. Integrated into popular detectors, our method delivers consistent gains. Compared with yolov8s, mAP50:95 of our method improved by 3.7% on VisDrone and 3.2% on MS COCO, respectively. Experimental results validate the effectiveness of the proposed module in significantly enhancing small object detection accuracy.

由于像素表示有限和空间分布不均匀，小目标检测仍然是一个关键的挑战。在缺乏足够的上下文信息的情况下，很难提取出有区别的、完整的特征来进行准确的检测。在分析现代探测器多尺度特征融合的基础上，提出了一种由并行残差融合模块（PRFM）和差分路径通道聚合模块（DPCAM）组成的多阶段路径聚合模块（MPAM）。通过将路径聚合操作分解为多个阶段，MPAM显著增强了特征映射容纳和处理上下文信息的能力。PRFM通过跳跃连接从多尺度特征图中捕获纹理和语义信息。此外，还增加了一个通道分支，以实现注意力权重在通道和空间维度上的动态分布。DPCAM通过通道扩展运算来平衡来自不同特征映射的通道和空间信息。此外，深度部分注意（Deep-wise Partial Attention， DPA）通过平衡局部和全局信息之间的权重，增强了复杂背景中小目标的特征表示能力。集成到流行的检测器，我们的方法提供一致的增益。与yolov8s相比，该方法的mAP50:95在VisDrone和MS COCO上分别提高了3.7%和3.2%。实验结果验证了该模块的有效性，显著提高了小目标的检测精度。

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引用次数: 0

Logarithmic-sum function constrained set-membership FxNLMS algorithm for active noise control 有源噪声控制的对数和函数约束集隶属度FxNLMS算法

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2026-04-01 Epub Date: 2026-01-09 DOI: 10.1016/j.dsp.2026.105905

Weigang Chen, Zhiyong Chen

In the field of active noise control (ANC), the traditional filtered-x normalized least mean square (FxNLMS) algorithm does not utilize the sparsity of the adaptive filter's weight vector, resulting in poor noise reduction performance. Additionally, when the reverberation time is long, the FxNLMS algorithm suffers from excessive computational load. To address the above two shortcomings of the FxNLMS algorithm, this paper proposes a logarithmic-sum function constrained set-membership FxNLMS (LSF-SM-FxNLMS) algorithm, which introduces a constraint and a logarithmic-sum function penalty to the cost function of the FxNLMS algorithm to reduce the computational load and utilize the sparsity of the adaptive filter's weight vector. A hardware-in-the-loop test bench was constructed to measure the actual primary and secondary paths. In this paper, the proposed algorithm is described and derived in detail, and its performance is analyzed through computer simulations based on the actual primary and secondary paths. Simulation results show that the proposed algorithm outperforms the traditional algorithms in terms of the noise reduction.

在主动噪声控制（ANC）领域，传统的滤波-x归一化最小均方（FxNLMS）算法没有利用自适应滤波器权向量的稀疏性，导致降噪效果较差。此外，当混响时间较长时，FxNLMS算法的计算量过大。针对FxNLMS算法的上述两个缺点，本文提出了一种对数和函数约束集隶属度FxNLMS （LSF-SM-FxNLMS）算法，该算法在FxNLMS算法的代价函数上引入约束和对数和函数惩罚，以减少计算量并利用自适应滤波器权向量的稀疏性。搭建了硬件在环试验台，对实际主、次路径进行了测量。本文对所提出的算法进行了详细的描述和推导，并基于实际主从路径进行了计算机仿真，分析了算法的性能。仿真结果表明，该算法在降噪方面优于传统算法。

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引用次数: 0