IEEE Signal Processing Letters最新文献

英文中文

MIMO Through-the-Wall Radar Micro-Doppler Signature Augmentation Method Based on Multi-Channel Information Fusion 基于多通道信息融合的MIMO穿墙雷达微多普勒特征增强方法

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

IEEE Signal Processing Letters

Pub Date : 2026-01-12 DOI: 10.1109/LSP.2026.3652951

Weicheng Gao;Shui Liu;Jinshuo Wang;Xiaodong Qu;Xiaopeng Yang

Through-the-wall radar (TWR) can monitor and analyze the motion characteristics and activity patterns of indoor human targets, with the advantages of non-contact, high flexibility and privacy protection. However, existing TWR human activity recognition (HAR) techniques developed based on single-channel radar contain limited Doppler information, making it difficult to achieve accurate recognition on data where the direction of human motion is not parallel to the radar observation. To solve this problem, in this letter, a multi-input-multi-output (MIMO) TWR micro-Doppler signature augmentation method based on multi-channel information fusion is proposed. First, a multi-channel Doppler profile feature fusion method based on multi-scale wavelets with low-rank decomposition is presented. Then, a motion parameter estimation method based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) global optimization is proposed, and the fused Doppler profile transformation is implemented using the obtained orientation of human motion. Numerical simulated and measured experiments demonstrate the effectiveness of the proposed method.

穿墙雷达（TWR）可以监测和分析室内人体目标的运动特性和活动模式，具有非接触、高灵活性和隐私保护等优点。然而，现有的基于单通道雷达的TWR人体活动识别（HAR）技术包含有限的多普勒信息，这使得在人体运动方向与雷达观测不平行的数据上难以实现准确识别。针对这一问题，本文提出了一种基于多通道信息融合的多输入多输出（MIMO） TWR微多普勒特征增强方法。首先，提出了一种基于低秩分解的多尺度小波多普勒特征融合方法。然后，提出了一种基于BFGS （Broyden-Fletcher-Goldfarb-Shanno）全局优化的运动参数估计方法，并利用得到的人体运动方向进行融合多普勒轮廓变换。数值模拟和实测实验验证了该方法的有效性。

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引用次数: 0

LRCPN: A Lightweight Parallel Scheme for Underwater Acoustic Modulation Recognition LRCPN：一种轻量级的水声调制识别并行方案

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

IEEE Signal Processing Letters

Pub Date : 2026-01-12 DOI: 10.1109/LSP.2026.3652122

Bingzhang Wu;Shaoxuan Li;Ziyao Pan;Rongxin Zhang;Wei Su

This letter proposes a lightweight parallel recurrent–convolutional scheme to improve generalization capability and recognition accuracy while maintaining low computational complexity in resource-constrained underwater acoustic channels. In this scheme, the lightweight convolutional network is used to extract time–frequency features, and the lightweight recurrent network with gated recurrent units is used to capture long-term temporal phase correlations, thereby alleviating the Doppler-induced phase rotation and inter-symbol interference in time-varying multipath underwater acoustic channels. Sea-trial data are collected during shallow-water sea trials with strictly separated training and evaluation datasets. Experimental results on ten underwater acoustic modulation types show that the proposed scheme improves recognition accuracy by 6.2% and reduces computational cost by 22.4%, while exhibiting stronger generalization capability compared with benchmark schemes.

本文提出了一种轻量级的并行递归卷积方案，在资源受限的水声信道中提高泛化能力和识别精度，同时保持较低的计算复杂度。该方案采用轻量级卷积网络提取时频特征，采用带门控递归单元的轻量级递归网络捕获长期时间相位相关性，从而减轻时变多径水声信道中多普勒诱导的相位旋转和符号间干扰。海试数据是在浅水海试期间收集的，训练数据集和评估数据集严格分离。在10种水声调制类型下的实验结果表明，与基准方案相比，该方案的识别精度提高了6.2%，计算成本降低了22.4%，同时具有更强的泛化能力。

引用次数: 0

Multimodal Cosine Similarity Transformer for Gloss-Guided Sign Language Recognition 多模态余弦相似度转换器用于光泽引导的手语识别

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

IEEE Signal Processing Letters

Pub Date : 2026-01-12 DOI: 10.1109/LSP.2026.3653403

Lu Li;Qinkun Xiao;Peiran Liu

Continuous sign language recognition (CSLR) requires fine-grained alignment between visual sequences and gloss annotations under weak supervision, which is challenged by modality heterogeneity and ambiguous frame-to-gloss correspondence. We propose a Multimodal Cosine Similarity Transformer (MMCST) to address these issues. MMCST integrates RGB and keypoint heatmap features via gated fusion, and aligns them with gloss embeddings through a Gloss-Conditioned Cosine-Normalized Attention (GCNA) mechanism that stabilizes cross-modal alignment. To further enhance semantic consistency, we introduce Gloss-aware Contrastive Regularization (GLCR). The fused representation is modeled by a cosine-similarity Transformer and decoded with CTC. Experimental results show that MMCST achieves consistent improvements over strong baselines, and ablation studies confirm the effectiveness of gated fusion, GCNA, and GLCR in improving semantic alignment and yielding smoother training dynamics.

连续手语识别（CSLR）需要在弱监督下对视觉序列和语义注释进行细粒度对齐，但这一过程存在模态异质性和模糊的帧-语义对应关系。我们提出了一个多模态余弦相似变压器（MMCST）来解决这些问题。MMCST通过门控融合集成了RGB和关键点热图特征，并通过光泽条件余弦归一化注意（GCNA）机制将它们与光泽嵌入对齐，该机制稳定了跨模态对齐。为了进一步增强语义一致性，我们引入了感知光泽的对比正则化（GLCR）。融合表示采用余弦相似度变压器建模，并用CTC进行解码。实验结果表明，MMCST在强基线上取得了一致的改进，消融研究证实了门控融合、GCNA和GLCR在改善语义对齐和产生更平滑的训练动态方面的有效性。

引用次数: 0

Local-CGFC: A Local Cumulant Generating Function Classification Rule 局部累积量生成函数分类规则Local- cgfc

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

IEEE Signal Processing Letters

Pub Date : 2026-01-12 DOI: 10.1109/LSP.2026.3652119

Bo Tang;Steven Kay;Kaushallya Adhikari

A classification rule based on the cumulant generating function of the training data, called the Cumulant Generating Function Classifier (CGFC), has been recently proposed, and has shown promising performance in terms of improved classification accuracy and robustness against noises. This paper first presents a new information-theoretical explanation of CGFC which indeed makes a classification by minimizing sample mutual information. The original CGFC is a type of global model, and a new variant, called Local-CGFC, is further introduced in this paper to achieve a local classification rule. Experimental studies on real-life datasets demonstrate the effectiveness of the proposed classifier and further illustrate its great potential for a number of real-world applications.

最近提出了一种基于训练数据的累积量生成函数的分类规则，称为累积量生成函数分类器（CGFC），并在提高分类精度和对噪声的鲁棒性方面表现出了良好的性能。本文首先提出了一种新的信息理论解释，即通过最小化样本互信息进行分类。原来的CGFC是一种全局模型，本文进一步引入了一种新的变体local -CGFC来实现局部分类规则。对现实数据集的实验研究证明了所提出分类器的有效性，并进一步说明了其在许多现实应用中的巨大潜力。

引用次数: 0

Toward Detecting Hidden Functionalities in Deep Learning Models 探索深度学习模型中的隐藏功能

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.2026.3651083

Guobiao Li;Sheng Li;Zhenxing Qian;Xinpeng Zhang

Deep functionality hiding is an emerging technique that embeds confidential or sensitive functions within seemingly benign deep learning models (DLMs), which perform ordinary machine learning tasks. This enables such models to execute covert tasks while remaining undetected. Despite the rapid progress in deep functionality hiding, countermeasures remain unexplored. In this paper, we propose Distribution Offset Analysis (DOA), a novel method for detecting hidden functionalities in DLMs. Our key insight is that the weight distribution of a benign DLM typically follows a Gaussian distribution, whereas a container DLM with hidden functionalities exhibits notable statistical deviations from this Gaussian pattern. In our methodology, we first compute the distributional distance (i.e., offsets) between the model's weights and an ideal Gaussian distribution. We then fuse these offsets with weight features into a unified representation, which is subsequently used to train a meta-classifier for hidden functionality detection. Through extensive experiments, we demonstrate the effectiveness of the proposed DOA method, which achieves an average detection rate of over 87% against existing state-of-the-art deep functionality hiding techniques.

深度功能隐藏是一种新兴技术，它将机密或敏感功能嵌入到执行普通机器学习任务的看似良性的深度学习模型（dlm）中。这使得这些模型能够在不被发现的情况下执行隐蔽任务。尽管在深度功能隐藏方面进展迅速，但对策仍未探索。本文提出了一种检测dlm中隐藏功能的新方法——分布偏移分析（DOA）。我们的关键见解是，良性DLM的权重分布通常遵循高斯分布，而具有隐藏功能的容器DLM则表现出与这种高斯模式的显著统计偏差。在我们的方法中，我们首先计算模型权重和理想高斯分布之间的分布距离（即偏移量）。然后，我们将这些偏移量与权重特征融合成一个统一的表示，随后用于训练用于隐藏功能检测的元分类器。通过大量的实验，我们证明了所提出的DOA方法的有效性，相对于现有的最先进的深度功能隐藏技术，该方法的平均检测率超过87%。

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引用次数: 0

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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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

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