检索结果-内蒙古大学图书馆

IEEE International Conference on information Communication and signal processing (ICICSP)

作者： Hailong Kang Haojie Zhao Liang Xu Jun Li Hangzhou Institute of Technology Xidian University Hangzhou China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

The extraction of the scattering center is a key step in the 3D imaging of interferometric inverse synthetic aperture radar (InISAR). Due to the influence of background noise and sidelobes, the existing methods often lead to inaccurate extraction of scattering center, resulting in outlier or loss of point clouds in the reconstructed 3D images. To solve this problem, we propose a robust scattering center extraction method based on range-azimuth profile reconstruction. First, the 1D range profile and the 1D azimuth profile are reconstructed based on autocorrelation matrix eigenvalue decomposition to suppress the background noise, and then the threshold is determined based on the proposed threshold updating mechanism to extract the scattering center accurately. Finally, the effectiveness and superiority of the proposed method are verified by the simulation results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Teacher Knowledge Distillation

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Te...

引用

signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Guang Zhang Zhaocheng Wang Ruonan Wang Hualiang Song Hailong Kang School of Electronics and Information Engineering Hebei University of Technology Tianjin China Hangzhou Institute of Technology Xidian University Hangzhou China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

As a critical branch of synthetic aperture radar (SAR) interpretation, SAR target detection has many applications in the military and civil fields. Although traditional deep learning methods achieve some satisfactory performance in target detection tasks, they inherently require significant computational resources and interpretation time. Hence, traditional deep learning methods frequently struggle to maintain a balance between high efficiency and precision. It is undoubtedly a huge challenge for target detection tasks. To address this problem, an efficient SAR target detector based on multi-teacher knowledge distillation (MTKD-Det) is proposed in this paper, which can effectively enhance target detection accuracy while reducing inference time. Specifically, a multiple-teacher knowledge distillation strategy is used in the proposed method, which integrates the experiences and methodologies of multiple teachers to collaboratively guide the training and learning of the student network, ultimately enhancing the robustness of the student network. In this work, a novel teacher network is developed to enhance the detection performance of the proposed method specifically for small targets. Furthermore, an attention mechanism is incorporated into the student network to enable it to focus on more critical information, thereby improving overall detection efficacy. Experimental results obtained from a measured SAR target detection dataset convincingly validate the benefits of the proposed approach.

关键词： Knowledge engineering Deep learning Training Attention mechanisms Accuracy Object detection Detectors Feature extraction Robustness Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Face Forgery Detection Algorithm Based on Improved MobileViT Network 8

Face Forgery Detection Algorithm Based on Improved MobileViT...

引用

8th International Conference on intelligent Computing and signal processing, ICSP 2023

作者： Wang, Tiantian Lu, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou014010 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou014010 China Inner Mongolia University of Technology Institute of Information Engineering Hohhot010051 China

ISBN: (纸本)9798350302455

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization. © 2023 IEEE.

关键词： National security

来源：评论

学校读者我要写书评

暂无评论

radar ANTI-JAMMING STRATEGY LEARNING VIA DOMAIN-KNOWLEDGE ENHANCED ONLINE CONVEX OPTIMIZATION

arXiv

引用

arXiv 2024年

作者： Liu, Liangqi Pu, Wenqiang Li, Yingru Jiu, Bo Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer’s characteristics. In this paper, the competition between a frequency agile radar and a Digital Radio Frequency Memory (DRFM)-based intelligent jammer is considered. We introduce an Online Convex Optimization (OCO) framework designed to illustrate this adversarial interaction. Notably, traditional OCO algorithms exhibit suboptimal sample efficiency due to the limited information obtained per round. To address the limitations, two refined algorithms are proposed, utilizing unbiased gradient estimators that leverage the unique attributes of the jammer system. Sub-linear theoretical results on both static regret and universal regret are provided, marking a significant improvement in OCO performance. Furthermore, simulation results reveal that the proposed algorithms outperform common OCO baselines, suggesting the potential for effective deployment in real-world scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline 2

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

引用

2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Zhou, Hanpu Yan, Kai Wang, Jian Li, Xiaoxiao Tian, Weiming Deng, Yunkai Bejing Institute of Technology Radar Technology Research Institute Beijing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Building Research Institute Corporation Limited Cscec Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798331515669

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data. © 2024 IEEE.

关键词： radar imaging

来源：评论

学校读者我要写书评

暂无评论

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATURES IN ENTOMOLOGICAL radar

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATU...

引用

IET International radar Conference 2023, IRC 2023

作者： Li, Biao Liu, Hanzhe Zhang, Tianran Cai, Jiong Wang, Rui The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

When tracking densely distributed targets such as insects, the traditional trajectory association algorithm exhibits poor correlation performance, leading to a decline in multi-target tracking efficiency. In this paper, based on the behavioral characteristics of insects gathering and co-orienting during aerial flight, a motion dynamic feature-assisted multi-target tracking method is proposed. Firstly, a three-dimensional biological distribution state matrix is constructed through spatial gridization. Then, a three-dimensional circular convolution is employed to estimate the target's motion dynamic feature, assisting in multi-target trajectory initialization. Finally, the improvement effect of the method on trajectory tracking accuracy is validated through simulations and migration insect data. © The Institution of Engineering & Technology 2023.

关键词： radar tracking

来源：评论

学校读者我要写书评

暂无评论

Fast Moving Target Focusing with FDMA radar

Fast Moving Target Focusing with FDMA Radar

引用

signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yanxing Wang Shi Yan Lan Lan Shengqi Zhu Lei Cui Guozhong Chen Ximin Li Yixuan Guo National Key Laboratory of Radar Signal Processing Xidian University Xian China Beijing Institute of Remote Sensing Device Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper deal with the challenge of focusing on fast-moving targets in a frequency division multiple access (FD-MA) radar system. At the modeling stage, the linear frequency modulation (LFM) waveform is transmitted by introducing a frequency increment (at least equal to the bandwidth) across the transmit array elements. The received signals are subsequently processed by a filter bank to separate the transmitted waveforms, which in turn provides additional degrees of freedom in the range domain. Particularly, a compensation procedure is designed to eliminate the incoherence between echos between different transmit element due to frequency diversity. To this end, as the outputs of different matched filters become coherent in the spatial domain, the keystone transform (KT) operation is employed to correct the range walk caused by the fast movement of the target, making it possible to focus the target in the space-time-range domain. Furthermore, the angle of the target is estimated and further utilized for beamforming, enhancing the range resolution. Numerical results are provided to verify the effectiveness of the proposed method in focusing the fast moving target with a FDMA radar.

关键词： Chirp modulation Array signal processing Frequency division multiaccess Spaceborne radar Focusing Phase frequency detectors Bandwidth Transforms Frequency diversity signal resolution

来源：评论

学校读者我要写书评

暂无评论

A Least-Squares-Based Algorithm for Estimating ATI SAR Interferometric Phase Errors

A Least-Squares-Based Algorithm for Estimating ATI SAR Inter...

引用

signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zhibin Wang Huancheng Guo Qingjun Zhang Zhang Qi Zhengfang Li Changshan He Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

ATI SAR systems have immense potential for high-resolution, wide-swath measurements of sea surface currents. However, due to the sensitivity of ATI interferometric phase to phase errors, the retrieval of sea surface currents using ATI technology often relies on ground scene calibration. To address this issue, we first review the principles of ATI SAR sea surface current measurement and analyze the sources and characteristics of phase errors. Based on this analysis, we propose a least-squares-based method for estimating and compensating ATI SAR phase errors. This method ensures robust estimation and correction of phase errors by accounting for their distinct distribution characteristics. Finally, we validate the proposed method using data from the PIESAT-1 satellite constellation, demonstrating its effectiveness.

关键词： Sea surface Satellite constellations Sensitivity Reviews Current measurement Estimation Sea measurements Data processing radar polarimetry Calibration

来源：评论

学校读者我要写书评

暂无评论

M²-Net: Multitask-Learning-Based Multiband signal Recognition Network

引用

IEEE Internet of Things Journal 2025年第11期12卷 16543-16558页

作者： Xingjian Zhang Pengxu Wang Yuan Ma Jian Jiao Shaohua Wu Qinyu Zhang Guangdong Provincial Key Laboratory of ACNT Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Boardband Communications Pengcheng Laboratory Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration the Guangdong Key Laboratory of Intelligent Information Processing and the College of Electronic and Information Engineering Shenzhen University Shenzhen China

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multitask-learning-based multiband signal recognition network $(\text {M}^signal\text {-Net})$ to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed $\text {M}^signal\text {-Net}$ consists of two main parts: 1) shared feature extraction network (SFEN) and 2) multitask classification header (MCH). In SFEN, a plug-and-play multitask feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multitask model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multiband signal dataset and compare $\text {M}^signal\text {-Net}$ with several state-of-the-art models in signal recognition field. Experiment results show that the proposed $\text {M}^signal\text {-Net}$ has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions.

关键词： Sensors Training Feature extraction Multitasking Wireless communication Accuracy Wireless sensor networks Pattern classification Narrowband Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Fast Node Selection of Networked radar Based on Transfer Reinforcement Learning 5th

Fast Node Selection of Networked Radar Based on Transfer R...

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Cao, Yanjun Wang, Yuan Guo, Jingjing Han, Li Zhang, Chao Zhu, Jin Zhang, Tianyang Zhang, Xiangrong The 54th Research Institute of China Electronics Technology Group Corporation Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Shaanxi Xian710071 China National Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9783031149023

The networked radar system can synthesize different echo signals received by various radars and realize the cooperative detection of multiple radars, becoming more and more critical for data fusion sharing and network collaboration. However, due to the large number and wide range of nodes in the networked radar system, there exists a redundancy problem in radar node assignment, which causes additional resource consumption and slows down the task execution speed of radar node selection. To solve the above problem, this paper proposes a fast radar node selection method based on transfer reinforcement learning to quickly select the optimal and minimum node resources. The proposed method devises a novel reward function for the Monte Carlo Tree and a different termination state of iteration to select the minimize the number of radar nodes. In order to further accelerate the selection of radar nodes, transfer reinforcement learning is presented to fully leverage the previous knowledge. Experimental results show that our proposed method can quickly select the optimal and minimum radar nodes in a brief period, significantly improving the speed of radar node selection in the networked radar. © 2022, IFIP International Federation for information processing.

关键词： Reinforcement learning

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：