IEEE Signal Processing Letters最新文献

英文中文

An Analytical Implementation of the Rosenblatt Transformation Rosenblatt变换的解析实现

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2026-01-06 DOI: 10.1109/LSP.2026.3651227

Steven Kay;Kaushallya Adhikari;Kaan Icer

A new approach to the analytical implementation of the Rosenblatt transformation is described. It leverages the properties of the empirical probability density function, which is the standard estimate of an unknown density. As such its utility is to applications where training data is available for the unknown density. These applications include data-driven algorithms for detection/classification and other statistical signal processing problems where the underlying probabilistic description of the data is unknown. As an illustration, an application to anomaly detection is described in detail using Gaussian and radar datasets.

描述了一种新的方法来分析实现罗森布拉特变换。它利用了经验概率密度函数的性质，这是未知密度的标准估计。因此，它的用途是用于未知密度的训练数据可用的应用程序。这些应用包括用于检测/分类的数据驱动算法和其他统计信号处理问题，其中数据的潜在概率描述是未知的。作为说明，详细描述了高斯数据集和雷达数据集在异常检测中的应用。

引用次数: 0

A Grating Lobes Suppression Method for MIMO Imaging Radar Based on Phase-Coherence-Guided Adaptive Threshold Classification 基于相参制导自适应阈值分类的MIMO成像雷达光栅瓣抑制方法

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2026-01-06 DOI: 10.1109/LSP.2026.3651005

Yiran Zhao;Jinze Li;Shisheng Guo;Zhuohang Shi

The sparse array configuration of multi-input multi-output imaging radar leads to high grating lobes problem in the imaging process, which significantly degrades final image quality. Although the traditional Phase Coherence Factor can partially mitigate these grating lobes, it suffers from limitations such as attenuation of the main lobe energy. To overcome these drawbacks, this paper proposes a novel grating lobes suppression method based on phase-coherence-guided adaptive threshold classification. This method first adaptively determines a classification threshold by analyzing the phase coherence features of the target main lobe. Using this threshold, all the grids in the radar image are classified into two categories and distinct schemes are applied to compute their respective weighting factors. Finally, grating lobes in the image are suppressed by weighting the original radar image. Numerical simulation and field experiment both confirm the effectiveness of the proposed method, which achieves a higher peak sidelobe ratio than conventional methods, demonstrating promising practical value.

多输入多输出成像雷达的稀疏阵列配置导致成像过程中存在高光栅瓣问题，严重影响最终成像质量。虽然传统的相位相干系数可以部分地缓解这些光栅瓣，但它受到主瓣能量衰减等限制。为了克服这些缺点，本文提出了一种基于相相干引导的自适应阈值分类的光栅瓣抑制方法。该方法首先通过分析目标主瓣的相位相干特性，自适应确定分类阈值；利用该阈值，将雷达图像中的所有网格划分为两类，并采用不同的方案计算各自的权重因子。最后，对原始雷达图像进行加权，抑制图像中的光栅瓣。数值模拟和现场实验均证实了该方法的有效性，其峰值旁瓣比高于常规方法，具有较好的实用价值。

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引用次数: 0

DDPTA: Zero-Shot Learning for Skeleton-Based Action Recognition 基于骨架的动作识别的零射击学习

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2026-01-02 DOI: 10.1109/LSP.2025.3650464

Jinjie Wang;Bi Zeng;Shenghong Zhong;Pengfei Wei;Xiaoting Gao

Traditional skeleton-based action recognition methods rely on large labeled datasets, which are costly to collect and unsuitable for hazardous actions, thereby limiting generalization. To overcome these limitations, recent works adopt zero-shot learning by using rich textual descriptions to guide the alignment and recognition of unlabeled skeleton features. However, these methods still struggle with similar actions (e.g., reading vs. writing), due to ambiguity arising from noise in both modalities. We propose the Discriminative Dual-Prototype TextAlignment (DDPTA) framework. Our framework introduces a novel dual-prototype design with tailored refinement strategies to effectively distill these two complementary prototypes. For the Spatial Prototype, our CycleSpatial module first distills the action’s core joint form from noisy spatial features, which is then guided by a Sieve-based Alignment. For the Temporal Prototype, our MambaTempo module leverages the Selective State Space Model to extract representations across distinct temporal stages, enabling fine-grained alignment with descriptions of different time periods. Extensive experiments demonstrate the superior performance of our method, showcasing its effectiveness in advancing the field of zero-shot skeleton-based action recognition.

传统的基于骨架的动作识别方法依赖于大型标记数据集，这些数据集收集成本高且不适合危险动作，从而限制了泛化。为了克服这些限制，最近的研究采用零射击学习，通过丰富的文本描述来指导未标记骨架特征的对齐和识别。然而，由于两种方式的噪声产生的歧义，这些方法仍然难以处理类似的动作（例如，读与写）。我们提出了判别双原型文本对齐（DDPTA）框架。我们的框架引入了一种新的双原型设计，并采用定制的改进策略来有效地提取这两个互补的原型。对于空间原型，我们的CycleSpatial模块首先从嘈杂的空间特征中提取动作的核心关节形式，然后由基于筛子的对齐引导。对于时间原型，我们的MambaTempo模块利用选择性状态空间模型（Selective State Space Model）来提取跨不同时间阶段的表示，从而支持与不同时间段的描述进行细粒度对齐。大量的实验证明了该方法的优越性能，证明了其在推进基于零射击骨架的动作识别领域的有效性。

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引用次数: 0

An Improved Sufficient Condition for Weighted $ell _{r}-ell _{1}$ Minimization 加权$ well _{r}- well _{1}$最小化的改进充分条件

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2026-01-01 DOI: 10.1109/LSP.2025.3650438

Jianwen Huang;Feng Zhang;Xinling Liu;Runbin Tang;Jinping Jia;Runke Wang

The weighted

$ell _{r}-ell _{1}$

minimization with weight

$alpha$

has been extensively employed to robustly estimate a high-dimensional sparse signal

$x$

coded by the underdetermined linear measurements

$y=Ax+z$

, where

$A$

and

$z$

are the measurement matrix and noise, respectively. In this paper, we demonstrate that if the restricted isometry constant (RIC)

$delta _{s}$

$A$

fulfills

$delta _{s}< 1/(1+3t/sqrt{5})$

, where

$t$

relies on sparsity level

$s$

for known model parameters

$alpha$

and

$r$

, then any sparse signal

$x$

are ensured to be robustly reconstructed through solving the weighted

$ell _{r}-ell _{1}$

minimization in the noisy situation. The gained condition is testified to be much better that the state-of-art ones.

权重为$alpha$的加权$ell _{r}-ell _{1}$最小化被广泛用于鲁棒估计由欠确定的线性测量$y=Ax+z$编码的高维稀疏信号$x$，其中$A$和$z$分别是测量矩阵和噪声。在本文中，我们证明了如果$A$的限制等距常数（RIC） $delta _{s}$满足$delta _{s}< 1/(1+3t/sqrt{5})$，其中$t$依赖于已知模型参数$alpha$和$r$的稀疏级$s$，那么通过解决在噪声情况下的加权$ell _{r}-ell _{1}$最小化问题，可以保证任意稀疏信号$x$被鲁棒重构。实验证明，所获得的条件比现有的条件好得多。

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引用次数: 0

Interpreting the Trispectrum as the Cross-Spectrum of the Wigner-Ville Distribution. 将三谱解释为维格纳-维尔分布的交叉谱。

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2026-01-01 Epub Date: 2025-12-05 DOI: 10.1109/lsp.2025.3640510

Christopher K Kovach, Stephen V Gliske, Erin M Radcliffe, Sam Shipley, John A Thompson, Aviva Abosch

The fourth-order time-invariant spectrum, or trispectrum, has a simple derivation as the cross-spectrum among frequency bands in the Wigner-Ville distribution (WVD). Viewed this way, the trispectrum gains intuitive meaning as a measure of the linear dependence of power across frequencies, which yields some insight into its structure and interpretation. We highlight, in particular, a two-dimensional subdomain as useful for identifying modulated oscillations when the modulating envelope is non-negative or lowpass. Spectral characteristics of the carrier and modulating signals are revealed along separate axes of a two-dimensional representation of this domain. The application of this framework, combined with a previously described additive decomposition technique for higher-order spectra, is demonstrated by the blind identification and separation of sleep spindles and beta bursts in EEG.

四阶时不变谱，或三谱，在Wigner-Ville分布（WVD）中有一个简单的推导作为频带间的交叉谱。从这个角度来看，三谱作为功率在频率上的线性依赖的度量，获得了直观的意义，这对其结构和解释产生了一些见解。我们特别强调，当调制包络线是非负或低通时，二维子域对于识别调制振荡是有用的。载波和调制信号的频谱特性沿着该域的二维表示的单独轴显示。结合先前描述的高阶谱的加性分解技术，通过脑电睡眠纺锤波和β爆发的盲识别和分离证明了该框架的应用。

引用次数: 0

Language-Prompted Dynamic Learning for Semi-Supervised Medical Image Segmentation 半监督医学图像分割的语言提示动态学习

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2025-12-31 DOI: 10.1109/LSP.2025.3649590

Ying Zeng;Jialong Zhu

Medical image segmentation is fundamental to clinical diagnosis and treatment planning, yet existing models are constrained by the scarcity of annotated data, which are costly and labor-intensive to obtain. Semi-supervised learning (SSL) mitigates this issue by leveraging large volumes of unlabeled data, but most SSL methods rely solely on visual cues and often fail to capture subtle structures or low-contrast regions common in medical imaging. To address this limitation, we present LanDy, a Language-Prompted Dynamic Learning framework for semi-supervised medical image segmentation. LanDy introduces textual semantics from medical descriptions to enrich visual representations and reduce the ambiguity of pseudo-labels. Concretely, textual embeddings dynamically modulate convolutional filters to provide context-aware feature extraction, while a text-guided refinement mechanism improves the reliability of pseudo-labels on unlabeled data. Extensive experiments on benchmark datasets demonstrate that LanDy consistently outperforms state-of-the-art SSL methods, delivering more accurate and robust segmentation under annotation-efficient settings.

医学图像分割是临床诊断和治疗计划的基础，但现有的模型受到标注数据稀缺的限制，这些数据的获取成本高，劳动强度大。半监督学习（SSL）通过利用大量未标记的数据缓解了这个问题，但是大多数SSL方法仅仅依赖于视觉线索，通常无法捕获医学成像中常见的细微结构或低对比度区域。为了解决这一限制，我们提出了LanDy，一个用于半监督医学图像分割的语言提示动态学习框架。LanDy从医学描述中引入文本语义，丰富了视觉表征，减少了伪标签的歧义。具体而言，文本嵌入动态调制卷积过滤器以提供上下文感知的特征提取，而文本引导的细化机制提高了未标记数据上伪标签的可靠性。在基准数据集上的大量实验表明，LanDy始终优于最先进的SSL方法，在注释高效的设置下提供更准确和健壮的分割。

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引用次数: 0

Scenario-Based Distributed Fusion Estimation for Uncertain Systems With Bounded Noise 具有有界噪声的不确定系统基于场景的分布式融合估计

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2025-12-31 DOI: 10.1109/LSP.2025.3649602

Changyong Xu;Bo Chen;Rusheng Wang;Zheming Wang

This letter investigates the distributed fusion estimation problem for uncertain systems, where noise statistics are unavailable. A scenario optimization framework is employed to handle model uncertainties, in which sampled uncertainty realizations are transformed into linear matrix inequality (LMI) constraints. By solving the resulting convex problems, local estimator gains are obtained, ensuring bounded mean-square error. Furthermore, an explicit upper bound for the fusion error is derived, and optimal fusion weights are determined through an LMI-based criterion. Finally, target tracking systems are provided to demonstrate the advantages and effectiveness of the proposed methods. The influence of the violation and confidence parameters on estimation accuracy and computational complexity is further analyzed.

本文研究了不可用噪声统计量的不确定系统的分布式融合估计问题。采用场景优化框架处理模型不确定性，将采样的不确定性实现转化为线性矩阵不等式约束。通过求解得到的凸问题，得到了局部估计量增益，保证了均方误差有界。进一步推导了融合误差的显式上界，并通过基于lmi的准则确定了最优融合权重。最后，以目标跟踪系统为例，说明了所提方法的优越性和有效性。进一步分析了违和参数和置信参数对估计精度和计算复杂度的影响。

引用次数: 0

Event-Based Dynamic Turbulence Mitigation 基于事件的动态湍流缓解

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2025-12-26 DOI: 10.1109/LSP.2025.3648967

Haoyi Zhao;Zeyu Xiao;Zihan Qi;Yang Zhao;Wei Jia

Atmospheric turbulence induces coupled spatio-temporal distortions, including blur, geometric deformation, and temporal jitter, which severely degrade image quality.We propose EvTurM, a practical framework leveraging event camera data for dynamic turbulence mitigation with precise motion cues and stable temporal modeling. Leveraging the high temporal resolution and dynamic range of events, EvTurM achieves robust restoration under diverse turbulence conditions. EvTurM comprises two key modules: (1) the event-aware modality enhancement module, which uses event-derived motion to enrich RGB features and recover structural details, and (2) the bidirectional modality calibration module, which jointly aligns RGB and event features in forward and backward propagation to reduce misalignment and enhance temporal consistency. Extensive experiments show EvTurM consistently surpasses existing methods and achieves superior performance.

大气湍流引起的时空耦合畸变包括模糊、几何变形和时间抖动，严重降低了图像质量。我们提出了EvTurM，这是一个实用的框架，利用事件相机数据进行动态湍流缓解，具有精确的运动线索和稳定的时间建模。利用事件的高时间分辨率和动态范围，EvTurM在各种湍流条件下实现了鲁棒恢复。EvTurM包括两个关键模块：(1)事件感知模态增强模块，该模块利用事件衍生的运动来丰富RGB特征并恢复结构细节；(2)双向模态校准模块，该模块将RGB和事件特征在前向和后向传播中联合对齐，以减少不对齐并增强时间一致性。大量的实验表明，EvTurM不断超越现有的方法，并取得了卓越的性能。

引用次数: 0

Multi-Kernel Maximum Asymmetric Correntropy Criterion: Foundation and Analysis 多核最大不对称熵准则：基础与分析

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2025-12-26 DOI: 10.1109/LSP.2025.3648910

Xiaoqiang Long;Haiquan Zhao;Xinyan Hou

Traditional single-kernel or fixed-center multi-kernel collaborative correntropies fundamentally assume that errors primarily cluster around a central point (typically zero). However, in real-world complex noise environments—such as those generated by mixed interference sources with diverse mechanisms—errors may exhibit multi-modal or highly asymmetric statistical characteristics. In such cases, a single central point or multi-kernels fixed at the origin cannot effectively capture the true shape of the error distribution. To address these problems, this letter proposes a novel robust learning algorithm by introducing variable-center multi-kernel correntropy into an asymmetric correntropy framework, where the kernel centers can be positioned at arbitrary locations. Compared with the maximum asymmetric correntropy criterion (MACC) algorithm, the proposed approach offers a more generalized formulation that enhances its capability to handle more complex error distributions, thereby improving algorithm performance. Notably, existing literature has not yet provided theoretical analysis for such variable-center multi-kernel asymmetric correntropy robust algorithms. Therefore, the main contributions of this work include: conducting the first theoretical analysis of the proposed algorithm, and validating the effectiveness of the analytical methodology.

传统的单核或固定中心多核协同熵从根本上假设错误主要围绕一个中心点（通常为零）聚集。然而，在现实世界的复杂噪声环境中，例如由多种机制的混合干扰源产生的噪声，误差可能表现出多模态或高度不对称的统计特征。在这种情况下，单一中心点或固定在原点的多核不能有效地捕捉误差分布的真实形状。为了解决这些问题，本文提出了一种新的鲁棒学习算法，通过将变中心多核熵引入非对称熵框架，其中核中心可以定位在任意位置。与最大不对称熵准则（MACC）算法相比，该方法提供了更广义的公式，增强了其处理更复杂误差分布的能力，从而提高了算法性能。值得注意的是，现有文献尚未对这种变中心多核不对称熵鲁棒算法进行理论分析。因此，本工作的主要贡献包括：对所提出的算法进行第一次理论分析，并验证分析方法的有效性。

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引用次数: 0

A Fractional-Order Cauchy Penalty With Enhanced Adaptability for Signal Recovery 具有增强信号恢复适应性的分数阶柯西惩罚

IF 3.9 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Signal Processing Letters

Pub Date : 2025-12-26 DOI: 10.1109/LSP.2025.3648640

Zhen Gao;Yiping Jiang;Rong Yang;Xingqun Zhan

Recovering signals from noisy observations remains challenging due to the ill-posedness of inverse problems. While non-convex regularization methods like the standard Cauchy penalty improve estimation accuracy, it lacks adaptability across diverse scenarios. In response, this letter proposes a fractional-order Cauchy (q-Cauchy) penalty inspired by the Lq maximum likelihood estimation. By introducing the parameter

$q$

, the q-Cauchy penalty achieves greater adaptability in diverse scenarios. Specifically, we also derive sufficient convexity conditions for its proximal operator and propose a forward-backward solver. Simulation results demonstrate that the q-Cauchy with the appropriate

$q$

outperforms the baseline methods in both 1D signal denoising and 2D image deblurring tasks.

由于逆问题的病态性，从噪声观测中恢复信号仍然具有挑战性。虽然像标准柯西惩罚这样的非凸正则化方法提高了估计精度，但它缺乏对不同场景的适应性。作为回应，这封信提出了一个分数阶柯西（q-Cauchy）惩罚，灵感来自Lq最大似然估计。通过引入参数$q$， q- cauchy penalty在不同的场景下具有更强的适应性。具体地说，我们还得到了它的近算子的充分凸性条件，并提出了一个正向向后求解器。仿真结果表明，适当的q- cauchy方法在一维信号去噪和二维图像去模糊任务中都优于基线方法。

引用次数: 0

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