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Chinese Control and Decision Conference, CCDC

作者： Xiuci Mo Teng Wang Weidong Hu Hairuo Zhang Xiaoyang Li Deyun Zhou National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

An optimization algorithm of multi-antenna array radar search parameters based on proximal policy optimization is proposed to solve the problem that the parameters of traditional single-antenna array radar optimization model is hard to solve in complex multi-antenna array scenario. Firstly, considering the constraints of multi-antenna array scenario, combined with the actual search task requirement of airborne radar, an optimization model of multi-antenna array radar search parameters based on the maximum expected discovery distance of the target is established. Secondly, the dual observation space and composite reward function with discount factor are introduced to transform the above optimization problem into a dynamic decision problem of reinforcement learning, in which the solution of model parameters is realized by executable action output by the policy network. Using the Actor-Critic algorithm structure, combined with methods for solving extreme value of function, the weight of each antenna array and the corresponding sub-airspace search resource allocation coefficient are updated online. Finally, the simulation results show that the proposed algorithm can quickly make accurate autonomous decision based on the airspace-target set covering model and the threat of each target, and the performance of proposed algorithm is significantly better than the traditional algorithm in the multi-antenna array scenario.

关键词： Phased arrays Optimization models Atmospheric modeling Simulation Airborne radar Reinforcement learning Search problems

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Inshore ship detection using high-resolution SAR images based on multi-feature fusion

Inshore ship detection using high-resolution SAR images base...

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IET International Radar Conference (IRC 2023)

作者： Shujie Wu Wei Wang Feng Ruan Huiqiang Zhang Jie Deng Pengcheng Guo Hongqi Fan Xi’an Electronic Engineering Research Institute Xi’an People's Republic of China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

The scattering characteristics of inshore ships in SAR images are similar to those of buildings and other man-made targets on land, making it difficult to detect them. In order to accurately detect the inshore ships in high-resolution SAR images, this paper proposes a multi-feature fusion method. Firstly, coarse sea-land segmentation is achieved by OTSU method and ROI (region of interest) is quickly determined by the maximum stability extremal region (MSER) method. After dividing the ROI into grid slices, the features of candidate targets and their surrounding backgrounds in the ROI are computed. And finally, background resolution (BR) is constructed by feature fusion to distinguish false alarm targets from real targets. Experiments based on satellite-based SAR data show that the method proposed in this paper has good detection accuracy and timeliness.

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Small target Detection Algorithm Based on Two-Stage Feature Extraction

Small Target Detection Algorithm Based on Two-Stage Feature ...

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Software Engineering and Computer science (CSECS), International Conference on

作者： Wei Li Zhilong Zhang Chuwei Li Jiangwei Zou National Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Hunan Changsha China Innovation Center for Advanced Detection and Warning College of Electronic Science and Technologym National University of Defense Technology Hunan Changsha China

In the field of target detection, the traditional general algorithms based on open data sets such as Imagenet and COCO can not detect small targets well because of their small size and insignificant features. Considering that the feature information of small targets is mostly retained in the shallow layer of the network, this paper adds a detection head on the shallow layer of the backbone network on the basis of the general target detection algorithm, and introduces the shuffle attention mechanism to deeply integrate the feature information from different layers, so as to enhance the expression ability of the network for small targets. Compared with the original network, mAP has improved by 6.25% on the VisDrone dataset. At the same time, considering the difficulty of extracting the feature information of small targets in single-stage detection, we propose a two-stage detection framework that can be used on any detection algorithm. We process the images into blocks according to the size characteristics of the targets, carry out interpolation and redetection on the region of interest with low confidence, which makes the recall ability of small targets significantly improved. When the inference confidence threshold is 0.5, compared with the original network, the performance is improved by 4.24 % , which can significantly improve the ability of small targets detection.

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Applying self-structured data learning algorithm to aerial infrared and visual images

Applying self-structured data learning algorithm to aerial i...

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Cyber Sensing 2017

作者： Li, Jenfeng Sam Ternovskiy, Igor Graham, James Ilin, Roman Georgia Institute of Technology AtlantaGA30332 United States Automatic Target Recognition Center Right State Research Institute BeavercreekOH45431 United States Sensors Directorate Air Force Research Laboratory Wright Patterson AFB OH45433 United States Riverside Research BeavercreekOH45431 United States

ISBN: (纸本)9781510608719

Previously, we proposed and implemented a Self-structuring Data Learning Algorithm. This realized software package and the concept are still progressing. Earlier, it was tested with synthetic data and exhibited interesting results. The objectives of this paper are testing the algorithm with raw infrared and visual images and updating the algorithm as required. We first performed registration transformation and detection from the images with an existing software package. We then registered the detections with the registration transformations from both infrared and visual images. The registered detections were delivered to the algorithm for target detection and tracking without modification. Results revealed inability to handle very noisy infrared image features. To overcome this problem, we developed multiscale grid processing to improve detection classification in the algorithm. This updated algorithm shows much better target detection and tracking with the real-world data. More algorithm enhancements are in work such as incorporating pattern recognition, classification, and fusion. © 2017 SPIE.

关键词： Learning algorithms

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The video SAR simulation algorithm based on the DEM and SBR

The video SAR simulation algorithm based on the DEM and SBR

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IET International Radar Conference (IRC 2023)

作者： Yongqiang Zhang Zhiyong Song Gang Lei XI’AN Electronic Engineering Research Institute XI’AN People's Republic of China National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science National University of Defense Technology Changsha People's Republic of China

ISBN: (数字)9781837240982

This paper presents a rapid video synthetic aperture radar (SAR) simulation algorithm aimed at addressing the challenge of acquiring video SAR images. The proposed method independently simulates both targets and ground clutter. Background clutter is generated using a digital elevation map (DEM), while target echoes are simulated using Shooting and Bouncing rays (SBR). By employing ray tracing techniques, occlusion areas for both background clutter and targets are computed, thereby ensuring that the simulation results are adaptable to various algorithm development requirements. Ultimately, through a series of fast algorithms and GPU acceleration, significant reduction in simulation time is achieved, enabling rapid simulation of multiple video SAR images. This research provides abundant data sources for the study of video SAR detection, tracking, and recognition algorithms, offering robust support for practical applications.

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Radiation pattern design of Perovskite light-emitting diodes by nanostructures 4

Radiation pattern design of Perovskite light-emitting diodes...

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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Ci, Qing Ren, Xingang Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan237000 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230039 China Anhui University Key Laboratory of Electromagnetic Environmental Sensing Department of Education of Anhui Province Hefei230039 China

ISBN: (纸本)9781733509619

Perovskite light-emitting diodes (PeLEDs) are drawing great interest due to its promising applications in both high-quality display and lighting. The incorporations of nanostructures into the device have widely used to enhance the light extraction efficiency (LEE). Besides, the spatial emission pattern of PeLEDs can also be modified, which is useful in a range of scientific and industrial applications. Herein, we studied the characteristics of PeLEDs with incorporation of perovskite or Ag nanogratings into electron transport layer with finite difference-time domain (FDTD) numerical simulation method. The results clearly demonstrated that the LEE has enhanced more than three times compared with conventional planar PeLEDs. We further found that the horizontal location of emission source can greatly manipulate the radiation pattern of Ag patterned PeLEDs. The results suggest that the employment of nanostructures opens the possibility of controlling emission light. Moreover, the findings shown here will be useful to performance enhancement of other optoelectronic devices. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Perovskite

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Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

arXiv

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arXiv 2022年

作者： Qi, Jiahao Gong, Zhiqiang Liu, Xingyue Bin, Kangcheng Chen, Chen Li, Yongqian Xue, Wei Zhang, Yu Zhong, Ping The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha410073 China The National Innovation Institute of Defense Technology Chinese Academy of Military Science Beijing100000 China The School of Computer Science and Technology Anhui University of Technology Maanshan243032 China

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations. In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect. Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner. Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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YO-DETR: A Lightweight End-to-End SAR Ship Detector Using Decoder Head without NMS

YO-DETR: A Lightweight End-to-End SAR Ship Detector Using De...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yue Guo Tianzhu Zhang Shiqi Chen Ronghui Zhan Luzhuo Li Jun Zhang National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China College of Information and Communication National University of Defense Technology Wuhan China Beijing University of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

A lightweight SAR ship detection algorithm is necessary to further meet the demands of military applications. This paper proposes an end-to-end efficient SAR ship target detection algorithm based on RT-DETR called YO-DETR. In the feature extraction network, a CNN-based backbone network is used to replace the transformer-based encoder structure, which retains the original feature extraction capability while reducing the number of parameters. Additionally, in order to retain the characteristic of long-distance feature dependency in transformers, the IRMB module is incorporated into the CNN network to enhance long-distance feature interaction. Finally, the introduction of the decoder head reduces the additional time overhead of traditional NMS during inference. Ultimately, the YO-DETR method achieves a 98.2% mAP on the SSDD dataset with only 5.37M parameters and 10.5M weight size, while the FPS (when batch size is set to 32) also achieves 220.4.

关键词： Visualization Accuracy Geoscience and remote sensing Object detection Feature extraction Transformers Encoding

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Comprehensive evaluation on ISAR anti-jamming effectiveness via ELECTRE-III

Comprehensive evaluation on ISAR anti-jamming effectiveness ...

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International Conference on Information and Automation (ICIA)

作者： Moufa Hu Lei Gao Zhiyong Zhang The State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics&Information System Luoyang Henan 471003 China Science and Technology on Automatic Target Recognition(ATR) National University of Defense Technology Changsha Hunan 410073 China

Anti-jamming effect evaluation on Inverse Synthetic Aperture Radar (ISAR) is an important issue for electronic warfare. Existing evaluation methods always use the change before and after jamming of one or more indicators (or characteristics parameters) to assess the effect of jamming, in which these indicators are obtained from 2-D ISAR image. Performance of these methods will descend if the quality of ISAR image is very poor or even the ISAR image cannot be obtained. To overcome this deficiency, a new comprehensive evaluation method based on Multiple Attribute Decision Making (MADM) theory aim to target recognition is presented. Firstly, the evaluation model of ISAR anti-jamming is build using MADM. Then the evaluation indicator system is proposed. And it is made up from three layers of ISAR imaging, including indicators of signal layer, indicators of image layer and indicators of decision-making. Relative weight of indicators is decides by Analytic Hierarchy Process (AHP) algorithm. At last the ELECTRE-III is used to evaluate the performance of ISAR anti-jamming. Simulation shows the reasonableness of the proposed model and method.

关键词： Jamming Silicon Signal to noise ratio Radar imaging

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ACapsGan: Generative Adversarial Network Based on Capsule Network and Attention Mechanism

ACapsGan: Generative Adversarial Network Based on Capsule Ne...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rubo Jin Jianda Cheng Shiqi Chen Jie Deng Wei Wang National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China School of Automation and Electrical Engineering Linyi University Linyi China College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Large-scale, diverse and high-quality data is the foundation and key to achieving good generalization in target detection and recognition for deep learning-based algorithms. Directly collecting synthetic aperture radar (SAR) image data faces the difficulty in acquisition and high costs. Traditional SAR image simulation methods are limited by geometric and electromagnetic computation errors in their modeling process, and the high computational burden as well. Generative adversarial networks (GANs) offer a new approach for SAR image generation, but they struggle to achieve satisfactory results in terms of image quality and diversity. In order to overcome this problem, we propose a new type of GAN to learn the spatial relationship of the targets more effectively. Taking the real SAR images as input, we extract the target information through the capsule network, perturb the extracted features and adopt the attention mechanism to improve the quality and diversity of the augmented data.

关键词： Attention mechanisms target recognition Image synthesis Perturbation methods Object detection Feature extraction Generative adversarial networks

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