检索结果-内蒙古大学图书馆

Mainlobe Deceptive Interference Suppression with Planar Frequency Diverse Array MIMO radar

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Mengdi Lu, Jiahao Xu, Jingwei Li, Shiyin China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China

Frequency diversity array (FDA) is capable of suppressing mainlobe deceptive interference with delays exceeding one pulse relative to the true target, known as inter-pulse interference. However, current FDA-based interference suppression methods are ineffective when both inter-pulse and intra-pulse interferences coexist. To this end, we propose a two-stage mainlobe deceptive interference suppression method with planar FDA multiple-input-multiple-output (PFDA-MIMO) radar: 1) Horizontal transmit spatial frequency compensation (HTSFC) is applied using the range information of each range bin to differentiate between inter-pulse interferences and target. Subsequently, data-independent beamforming is performed to suppress inter-pulse interferences in horizontal spatial frequency domain;2) To distinguish the target from intra-pulse interferences, vertical transmit spatial frequency compensation (VTSFC) is applied utilizing the initial range of the range bin containing target. Afterwards, an adaptive beamforming (ABF) approach based on transmit-receive spatial smoothing (TRSS) is applied to suppress intra-pulse interference while avoiding target cancellation. Furthermore, the frequency increments in both rows and columns of PFDA are elaborately designed to effectively distinguish between true target and interferences. Extensive simulations are conducted to evaluate the excellent suppression effect of the proposed scheme against mainlobe deceptive interference. © 2025 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Hybrid signal fusion for target detection in distributed PA-MIMO radar systems on moving platforms

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Greco, Maria Sabrina Gini, Fulvio Xidian University National Key Laboratory of Radar Signal Processing Shaanxi Xi'an710071 China Science and Technology on Underwater Test and Control Laboratory Dalian116013 China University of Pisa Department of Information Engineering Pisa56122 Italy

Distributed phased multiple-input multiple-output (PA-MIMO) radar systems on moving platforms combine the spatial diversity of MIMO radars with the coherent gain of phased-array radars, making them ideal for dynamic environments. However, challenges such as motion-induced channel variability, Doppler shifts, and complex parameter estimation often hinder target detection. Traditional methods like non-coherent fusion (NCF) and coherent fusion (CF) struggle to adapt to such variability, leading to suboptimal performance. In this paper, we propose a hybrid signal fusion (HSF) scheme that integrates joint space-time-phase compensation (JSTPC) with a compressed fusion network. The JSTPC module mitigates motion-induced phase errors and ensures precise alignment of received signals, while the compressed fusion network combines CF and NCF stages, leveraging their respective strengths to adaptively manage channel correlation variability. We also develop a joint parameter estimation algorithm to accurately extract direction of departure, direction of arrival, and Doppler frequency, thereby ensuring precise signal reconstruction and enhancing the detection process by estimating key parameters in real time. Theoretical analyses of detection thresholds and probabilities, derived using moment-matching techniques, further validate the reliability of the proposed scheme. Monte Carlo simulations and theoretical analyses for diverse radar configurations and channel correlation scenarios show that the proposed HSF scheme achieves 2 dB detection gain improvement over NCF. © 2025 IEEE.

关键词： Frequency estimation

来源：评论

学校读者我要写书评

暂无评论

Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. Researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. Research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage Iteratively Reweighted Least Squares Estimator for Robust Elliptic Localization in the Presence of Impulsive Noise

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhao, Xiaoyu Lu, Zukun Li, Jun Huang, Long Yu, Yi Ou, Gang The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China The Key Laboratory of Satellite Navigation Technology Changsha410073 China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Hunan Xinghe Electronics Co.LTD Changsha410073 China

As a range-based localization approach, elliptic localization based on bistatic ranges (BRs) has wide range of applications in multistatic systems. This paper addresses the problem of robust elliptic localization using the minimum `pnorm criterion in scenarios affected by impulsive α-stable noise. To solve the `p-norm formulation, we develop an asymptotically efficient two-stage iteratively reweighted least squares (TSIRLS) estimator via incorporating pseudolinear estimation and multistage weighted least squares estimation into the framework of iteratively reweighted least squares (IRLS) methods. The estimator consists of two cascaded `p-norm minimization estimators. To obtain a closed-form solution at each iteration, we transform nonlinear BR measurement equations into pseudolinear forms through introducing auxiliary variables, thus resulting in an iteratively reweighted pseudolinear least squares (IRPLS) estimator in the first stage. The dependencies of unknown parameters and the estimate error derived from the IRPLS are utilized in the second stage to enhance the performance of target location estimate. The analytical derivation demonstrates that, under the assumptions of a sufficiently large number of measurements and small noise, the final position estimate obtained from the TSIRLS achieves the theoretical covariance of the general `pnorm minimization estimation. Extensive numerical simulations highlight the advantages of TSIRLS over existing least squares and robust estimators in terms of positioning performance and runtime. The TSIRLS is also observed to generate approximately unbiased estimates with mean square errors that closely approach the Cramér-Rao lower bound. © 2025 IEEE.

关键词： Impulse noise

来源：评论

学校读者我要写书评

暂无评论

Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning

arXiv

引用

arXiv 2025年

作者： Peng, Zhong Xu, Yishi Wang, Gerong Chen, Wenchao Chen, Bo Zhang, Jing National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Research Institute of Systems Engineering Academy Military Science Beijing100089 China

Compositional Zero-Shot Learning (CZSL) enables models to recognize novel compositions of visual states and objects that not emerge during training. Existing methods struggle with generalization due to the complex entanglement of states and objects in visual representations, limiting their ability to handle unseen compositions. To address this challenge, we propose Duplex (Dual Prototype Learning), a novel approach that employs a well-designed dual-branch architecture to integrate both semantic and visual prototypes, thereby facilitating efficient composition learning. Duplex utilizes a graph neural network (GNN) to adaptively update visual prototypes, capturing intricate interactions between states and objects. It then leverages the strong visual-semantic alignment of pre-trained vision-language models (VLMs) and incorporates a multi-path architecture with prompt engineering to align image and text representations, ensuring robust generalization. Extensive experiments on three benchmark datasets—MIT-States, UT-Zappos, and C-GQA—demonstrate that Duplex outperforms the state of the art across all datasets in both closed-world and open-world settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm

引用

Journal of Information and Intelligence 2025年第2期3卷 154-172页

作者： Shan Gao Lei Zuo Xiaofei Lu Bo Tang National Key Laboratory of Radar Signal Processing Xidian University Xi'an 710071 China Jiuquan Satelite Launch Centre Jiuquan 735400 China College of Electronic Engineering National University of Defense Technology Hefei 230037 China

Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.

关键词： Task allocation Heterogeneous agent models Heuristic algorithm Combination optimization

来源：评论

学校读者我要写书评

暂无评论

Segmentation of CAD models using hybrid representation

引用

虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

来源：评论

学校读者我要写书评

暂无评论

MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Difference Bonds Consistency and Complementarity to Enhance Multimodal Representation Learning

Difference Bonds Consistency and Complementarity to Enhance ...

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Congbing He Sensen Song Zhenhong Jia Hui Zhao Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal representation learning, existing research has primarily focused on exploring modal consistency and modal complementarity, while overlooking the positive role of modal difference. Moreover, modal difference establishes a bonding relationship between modal consistency and modal complementarity. However, existing algorithms lack the study of this relationship, resulting in an accuracy that still needs to be improved on multimodal classification tasks. To tackle the above issues, We propose a novel multimodal representation learning framework. It enhances multimodal representation learning by modal difference constructing connecting bonds of modal consistency and modal complementarity. We conducted experiments on two widely used multimodal emotion recognition datasets, IEMOCAP and MELD. The results demonstrate that our method outperforms existing multimodal representation learning approaches in terms of accuracy on the multimodal emotion recognition task.

关键词： Representation learning Emotion recognition Accuracy signal processing algorithms Speech recognition signal processing Acoustics Classification algorithms Speech processing Bonding

来源：评论

学校读者我要写书评

暂无评论

MFDPonzi: Detecting Ethereum Ponzi Schemes Using Static Features from Novel Opcode Sequences

MFDPonzi: Detecting Ethereum Ponzi Schemes Using Static Feat...

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Longwei Cao Jiwei Qin Xuzi Zhang College of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Schemes (or Ponzi Contracts) and have caused huge financial losses. Current Ponzi contract detection models face three main challenges: simple opcode sequence processing does not effectively distinguish Ponzi from non-Ponzi contracts, single-feature-based models lack accuracy, and reliance on transaction records hinders early detection. To address these issues, this paper proposes a Multi-Feature Ponzi Scheme Detection Model (MFDPonzi). MFDPonzi tracks the changes in stack, memory, and storage parameters during the execution of smart contracts, reconstructing opcode sequences and extracting diverse features, including semantic and developer features. Finally, a multi-feature fusion algorithm is used to enhance model stability. Additionally, MFDPonzi can identify Ponzi contracts at the early stage of smart contract creation without relying on transaction data. Experimental results show that MFDPonzi achieves an 85.9% recall and an 88.7% F-score on Ethereum smart contracts, outperforming baselines in both performance and robustness.

关键词： Biological system modeling Smart contracts signal processing algorithms Feature extraction Robustness Stability analysis Blockchains Logic Speech processing Thermal stability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：