检索结果-内蒙古大学图书馆

4th International Conference on Signal Processing and Computer science, SPCS 2023

作者： Li, Yongqian Zhang, Yu Zhang, Yichuang Qi, Jiahao Zhong, Ping National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510672505

Although deep learning has developed rapidly in recent years, the existence of adversarial examples casts a shadow over its future. Deep neural networks (DNNs) can be unreliable when facing these carefully crafted adversarial examples, limiting their real-world applications. While adversarial examples usually lack robustness and tend to lose effect when the viewing angle is changed. Moreover, it is impossible to control the angle at which adversarial examples are seen by the airborne detector. Therefore, we propose a multi-view robust adversarial attack by introducing perspective transformation and feature similarity loss to mitigate the negative effect that viewing angle changes bring to the features. We evaluated our adversarial patch on our multi-view test dataset and the results show that our proposed method reduced the mAP by 73.4%. Furthermore, compared to other methods, our approach performs best in the data with low depression angles, demonstrating its excellent multi-view robustness. © 2023 SPIE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Enhanced Deeplab Network for Infrared Small target Detection 8

Enhanced Deeplab Network for Infrared Small Target Detection

引用

8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Yao, Jiping Xiao, Shanzhu Tao, Huamin Wen, Gongjian Zhang, Luping Meng, Ying National University of Defense Technology Key Laboratory of Automatic Target Recognition Changsha China

ISBN: (纸本)9798350397932

With the development of deep-learning methods, convolutional neural networks are widely used to detect infrared small targets. But as the pooling layers get deeper, detailed information about infrared small targets will be lost since the infrared targets are dim and small. Thus we proposed an infrared small target detection network based on the deeplabv3+ model. In the proposed model, high-level semantic information and low-level high-resolution information are fused to extract the feature of infrared targets. Besides, SA attention module is adopted to extract the feature of infrared targets more precisely and suppress the noise of the background by using an attention mechanism before fusion. Experiments on open-source dataset sirstaug prove that our method EDeeplab is superior to other state-of-the-art methods. © 2023 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Dual Space SVDD Based Radar target recognition from HRRP

引用

Chinese Journal of Electronics 2025年第2期23卷 419-425页

作者： Huaitie Xiao Guoyu Feng Yongfeng Zhu Mengjun Huang Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha China

In radar target recognition based on kernel method, Support vector data description (SVDD) has been applied to High resolution range profiles (HRRPs) recognition. In this paper, first, three distribution models, i.e., Membership model, Cloud model, Gaussian mixture model, are developed to describe distribution characteristics of HRRPs in extended space. Secondly, test HRRPs in multi-target hypersphere spaces are classified, in accordance with their multi-space distributing characteristics, into two types, i.e., shrink sample and slack sample. Determination of the property of a test sample is achieved by using the minimum relative distance for shrink samples and three distribution models for slack samples, respectively. Thus three HRRP recognition methods based on dual space SVDD are formed. The extensive experiment results for HRRPs of four planes show that the proposed methods have better recognition performance than the recognition method based on single space SVDD.

关键词： target recognition Spaceborne radar Mixture models Vectors Data models Character recognition Kernel Gaussian mixture model Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

recognition of UAV Image Transmission Signal via Deep Learning Methods

Recognition of UAV Image Transmission Signal via Deep Learni...

引用

International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Zhang, Shunjie Wei, Wang Jun, Zhang National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811966125

In the last decade, the rapid development of UAV (Unmanned Aerial Vehicle) enterprises has given rise to a great number of drones, and a part of them illegal flights. Due to their low threshold of entry into the air, it is difficult to monitor low and slow UAVs which threaten the airspace safety. Determining the behaviour of UAVs is a challenging area of airspace regulation. Nowadays, DL (Deep Learning) has become a key research component of radiation signal recognition in many regulatory applications. In order to facilitate the solution to illegal UAV intrusion into airspace without locating the perpetrators, we apply deep learning methods in radio signal recognition based on a real-world signal dataset. Further the model of CNN (Convolutional Neural Network) was pruned to obtain a lightweight network for efficient signal recognition. In this paper, we have verified the first time the effectiveness of DL in recognizing drone raw signals. Then, we also take a closer look at the performance of lightweight models and compare acan achieve 98.9% recognition accuracy, which is at least 1.59% higher than CNN. Finally, we discuss open issues in this field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Image defogging algorithm based on polarization characteristics of target and atmospheric transmission model 9

Image defogging algorithm based on polarization characterist...

引用

9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Feng, Ling Yan, Zhang ZhiGuang, Shi Jinghua, Zhang Yi, Zhang National Key Laboratory of Science and Technology on Automatic Target Recognition School of Electronic Science and Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510664432

Aiming at the problem of image quality degradation caused by the scattering of particles in the atmosphere under foggy conditions, a polarization dehazing algorithm combining the target polarization degree and atmospheric transmission model is proposed in this paper. Firstly, a bilateral filtering method combined with image gradient information is proposed to solve four target light intensity images from different angles, and the filtered images are used to solve the polarization degree of the target. This method can preserve the edge texture information of the target in the filtering process and effectively improve the quality of the reconstructed image. Secondly, when estimating the atmospheric light intensity at infinite distance, an alpha filtering method combined with bright channels is proposed to avoid the interference of over-bright noise points in the image. The method can effectively suppress the atmospheric light intensity at infinite distance. By analyzing the experimental data, the average gradient and gray variance of the fog-free reconstructed image are significantly improved compared with the original image. Experimental results show that the proposed algorithm has strong defogging ability, and can effectively improve the image quality of the optical imaging system in foggy scenes, and realize the restoration of foggy images. © 2023 SPIE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

FPGA implementation of a Bernoulli particle filter

引用

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2025年第2期47卷 398-405页

作者： Lian, Hongfei Li, Dongsheng Jiang, Yanwen Fan, Hongqi Xiao, Huaitie Wang, Guoyan National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Aiming at the high-speed and efficient computing problem of Bernoulli particle filters in embedded application environments, taking the Bernoulli particle filters for joint detection and estimation of radar weak targets as an example, a function modularized and particle size scalable field programmable gate array (FPGA) implementation architecture is proposed. The computing speed of filtering calculation is further improved through approaches as particle state pipelining, layered accumulation and sum, and parallel resampling, etc. Xilinx ZC706 evaluation board on-board testing experiments have demonstrated the good scalability and excellent acceleration ratio of the proposed architecture. When the number of particles is 1 024, the acceleration ratio is about 104 orders of magnitude compared to the Intel Core i3-4130 CPU computing environment. The results have important reference value for the application of Bernoulli particle filtering technology in radar, robotics, and navigation guidance fields. © 2025 Chinese Institute of Electronics. All rights reserved.

关键词： Electronic guidance systems

来源：评论

学校读者我要写书评

暂无评论

Few-shot unsupervised domain adaptation for SAR target recognition based on input alignment and class confusion loss 2

Few-shot unsupervised domain adaptation for SAR target recog...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Shu, Wenyu Jiang, Chunyun Zhan, Ronghui Guo, Yue Chen, Shiqi National University of Defense Technology National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology Changsha China National University of Defense Technology College of Information and Communication Wuhan China

ISBN: (纸本)9798331515669

With the development of simulation technologies and deep learning, utilizing simulated images to train Synthetic Aperture Radar (SAR) target recognition model has become a research hotspot. However, SAR target recognition is hindered by the domain gap between simulated and real images, particularly under few-shot settings which mean only a limited number of unlabeled target domain samples are available for training. Regarding this issue, this paper proposed a new few-shot Unsupervised Domain Adaptation (UDA) method based on input alignment and class confusion loss (named IACC). Our method consists of two modules. The input alignment module (IAM) utilizes a special image-to-image translation model which can realize realistic image translation in few-shot settings, while the Class Confusion Module (CCM) employs a novel loss function to align the feature space and enhance classifier performance. Experimental results on the SAMPLE dataset demonstrate the superior performance of our method in both UDA and few-shot UDA scenarios compared with other existing methods. IACC offers a promising solution for SAR target recognition in challenging domain adaptation scenarios with limited data. © 2024 IEEE.

关键词： Radar simulators

来源：评论

学校读者我要写书评

暂无评论

Enhancing Image Perception Quality: Exploring Loss Function Variants in SRCNN 24

Enhancing Image Perception Quality: Exploring Loss Function ...

引用

2024 7th International Conference on Computer Information science and Artificial Intelligence, CISAI 2024

作者： Xiao, Yifan Li, Zhouli Zhang, Zhilong Xi’an Shiyou University Shaanxi Xi’an China National Laboratory of Automatic Target Recognition National University of Defense technology Hunan Changsha China

ISBN: (纸本)9798400707254

Convolutional Neural Networks (CNN) have made significant strides in the field of image super-resolution, with SRCNN being a classic exemplar. SRCNN employs Mean Squared Error (MSE) as its loss function and supervises the network on a per-pixel basis. Nonetheless, research into the mechanisms of visual perception and objective evaluation metrics for super-resolution images remains inadequate. To align subjective and objective assessments, this study employs perceptual metrics to evaluate the effect of image super-resolution. Perceptual metrics mimic the early transformation processes of the human visual system, utilizing the NLPD (Normalized Laplacian Pyramid Distance) between images and the original scene radiance to assess the super-resolution effect. Building upon this, we transition SRCNN’s loss function from MSE to two components, MAE (Mean Absolute Error) and NLPD. Using MAE as a measure of content accuracy and NLPD as a measure of perceptual similarity, we reconstruct high-fidelity super-resolution images by minimizing the NLPD between images and the original scene radiance. Experimental results indicate that, compared to SRCNN and BICUBIC, the super-resolution images we obtain not only restore content realistically but also align more closely with human visual perception. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Time Jitter DDMA MIMO Automotive Radar Waveform 1

引用

11th International Conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Liu, Jianhu Lian, Hongfei Chen, Qiao Chen, Sijia Beijing Rxbit Electronic Technology Co. Ltd. Beijing China National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China

ISBN: (数字)9789819923625

ISBN: (纸本)9789819923618

Aiming at the problems of low utilization of time and space and Doppler ambiguity of high-velocity targets in the traditional multiple input multiple output (MIMO) waveform of automotive radar, a time jitter Doppler frequency division multiplexing automotive radar waveform is proposed. This waveform jitters the pulse repetition interval stagger in the time domain, uses the phase difference between odd and even sequences to realize velocity ambiguity resolution, and realizes waveform orthogonality in the frequency domain through unequal spacing Doppler frequency modulation between multiple antennas, which improves the time-space transmission efficiency. In addition, an echo separation method based on non coherent accumulation is proposed, which can effectively solve the problem of target matching under Doppler frequency division multiplexing waveform. The effectiveness of the proposed waveform and signal processing method is verified by simulation experiments, and the waveform has strong application value in automotive radar engineering practice. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MIMO systems

来源：评论

学校读者我要写书评

暂无评论

Open-set HRRP recognition method based on joint sparse representation 4

Open-set HRRP recognition method based on joint sparse repre...

引用

2022 4th International Conference on Wireless Communications and Smart Grid, ICWCSG 2022

作者： Qu, Shuang Liu, Shengqi Fu, Qiang National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology China

Aiming at the problem of multi-view high-resolution range profile (HRRP) target recognition under open set conditions, we proposed an open set recognition method based on joint sparse representation (JSR), which solves the problem of low recognition rate of traditional methods under open set conditions. This method is applied to the background of radar single-station observation. JSR is used to solve the reconstruction error of multi-view HRRP by the over-complete dictionary, while extreme value theory (EVT) is used to model the reconstruction error tailing of matching and non-matching categories and transform the open set identification problem into a hypothesis testing problem. During recognition, we use the reconstruction error to determine the candidate class, the scores of the matching class and non-matching class are obtained according to the confidence of tail distribution, and the weighted sum of the two is used as the category criterion to finally determine the target or candidate class outside the library. This method can effectively use the relevant information between multi-view HRRPs to improve the performance of HRRP recognition under open set conditions. The algorithm is tested with HRRP data generated from MSTAR inversion, and the results show that the performance of the proposed method is better than the mainstream open set recognition method. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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：