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检索条件"机构=Science and Technology on Automatic Target Recognition Laboratory"

共 295 条记录，以下是251-260 订阅

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

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

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Training Deep learning Adaptation models Translation Image recognition target recognition Data processing Radar polarimetry Robustness Synthetic aperture radar

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Design and implementation of wideband all digital array radar test-bed

Design and implementation of wideband all digital array rada...

引用

European Radar Conference (EURAD)

作者： Yue Zhang Qinglong Bao Juhong Wu Songlin Li Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha People's Republic of China College of Electronic Science and Engineering National University of Defense Technology Changsha People's Republic of China

This paper presents a test bed for a wideband alldigital array radar, which consists of 32 linear array apertures. The operating frequency band is 1-3GHz and the instantaneous bandwidth is 500MHz. High-speed direct RF digitization and powerful digital signal processors are utilized in the design of the system. The test bed has been implemented now with a 32-elements array antenna and 8 digital receiver channels. Some experiments have been conducted and the testing results are presented in this paper. The testing results confirm the feasibility of the test bed, and guide us to carry on the remaining work of the whole system designing.

关键词： Arrays Radar Wideband Field programmable gate arrays Radio frequency Receivers Broadband antennas

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Design and implementation of wideband all digital array radar test-bed

Design and implementation of wideband all digital array rada...

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European Conference on Microwave

ISBN: (纸本)9781479955985

关键词： Arrays Wideband Radar Field programmable gate arrays Radio frequency Receivers Broadband antennas

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Coherent Integration Method of Frequency Agile Radar for target Detection

Coherent Integration Method of Frequency Agile Radar for Tar...

引用

IEEE Asia-Pacific Conference on Antennas and Propagation

作者： Caiyong Lin Ruiqi Tian Qinglong Bao Zengping Chen Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology

ISBN: (纸本)9781538616093

Frequency agile radar (FAR) has been widely used due to its good ability in anti-jamming and low probability of interception. However, FAR transmits pulses with agile frequencies, which will lead to random phase fluctuation and destroy the consistency between pulse-to-pulse radar echo. Aiming at this problem, a coherent integration method for target detection using FAR is proposed in this paper. This method first removes the initial carrier frequency of echo signals. Then a bank of matched filters are constructed according to the echo signals with initial carrier frequency removed. After being matched filtered, the phase fluctuation due to frequency agility is eliminated. Consequently, coherent integration can be achieved by feeding the filtered signals to fast Fourier transform (FFT). Finally, numerical simulations are performed to verify the effectiveness of the proposed method.

关键词： Radar Matched filters Fluctuations Frequency modulation Doppler effect OFDM Object detection

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Method of Multi-Channel Calibration for Digital Array Radar

Method of Multi-Channel Calibration for Digital Array Radar

引用

European Microwave Conference

作者： Weixing Li Jianzhi Lin Weihua Wang Yasen Wang Zengping Chen Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology

ISBN: (纸本)9781467392709

Amplitude and phase errors among multi-channels in digital array radar will seriously deteriorate the performance of digital beam-forming (DBF) and high resolution direction finding. This paper describes a practical calibration algorithm that calibrates amplitude and phase errors coarsely using interior calibration source and then realizes precise calibration by eigen-decomposition approach. Based on an 8-elements digital array radar test-bed operating at L & S band, experiments are carried out in the anechoic chamber and outdoors. After calibration, the radiation pattern is nearly ideal and the direction of arrival (DOA) estimation of measured data achieves a good performance. The expected results illustrate that interior-auto calibration algorithm is fairly effective and feasible in actual systems.

关键词： Digital Array Radar Amplitude and phase errors Interior-auto calibration Eigen-decomposition Direction of arrival

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A Triplet Branch End-To-End Framework for Long-Tailed Object recognition in Remote Sensing Images

A Triplet Branch End-To-End Framework for Long-Tailed Object...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shiqi Chen Yue Guo Rongqiang Zhu Pan Chen Weiwei Zhao College of Information and Communication National University of Defense Technology Wuhan China National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In the real world scenarios, the classes of many remote sensing images typically exhibit an imbalanced distribution, where only some classes occupy abundant samples while the other classes have decreasing number of samples. Although recent studies on SAR target recognition have achieved substantial progress based on deep learning models, they still suffer from an obvious performance drop on the tail classes. Therefore, how to improve the long-tailed SAR target recognition performance is a promising and challenging topic. In this work, we explore an improved contrastive learning based end-to-end training framework, which consists of triplet branch to enable balanced feature representation and less biased classifier. Specifically, the recalibrated prototype is introduced to handle data imbalance and assist contrastive learning. The optimization of two sub-tasks is achieved by a loss scheduler to progressively transmit the learning priorities. Experimental results on long-tailed SAR image dataset and optical remote sensing dataset demonstrate that our approach obtains remarkable performance boosts on tail classes and also achieves the overall state-of-the-art performance.

关键词： Training target recognition Prototypes Contrastive learning Tail Propagation losses Radar polarimetry

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target Detection Based on Phase Noise of Repeating Jamming

Target Detection Based on Phase Noise of Repeating Jamming

引用

Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Hui Jiang Xiaoyong Du Weidong Hu Ziqiao Yuan Xi'an Electronic Engineering Research Institute Xi'an China National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China

The repeating type of false target enters the radar receiver through the main lobe, realistically simulating the waveform of the real target in the time and frequency domains, posing a serious threat to radar search, recognition, and tracking. To improve the detection performance of radar in the above-mentioned interference scenarios, this paper models and analyzes the phase noise of the local oscillator of the jammer based on the phase noise difference between the jammer and the radar radio frequency (RF) system. On this basis, by establishing a statistical model for repeating jamming phase noise, utilizing the difference in phase noise between target echo and jamming signal, and based on Neyman Pearson (NP) theorem and generalized likelihood ratio tests (GLRT) method, a discriminative criterion is designed to achieve target detection in the background of interference. The effectiveness and feasibility of the algorithm are verified by Monte Carlo simulation results.

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Calibration of Polarimetric Radar System with Giant Aperture Antenna

Calibration of Polarimetric Radar System with Giant Aperture...

引用

European Microwave Conference

作者： Jianzhi Lin Weixing Li Yue Zhang Qianqiang Lin Zengping Chen Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology

ISBN: (纸本)9781467392709

This paper describes a convenient technique for polarimetric calibration of a polarimetric inverse synthetic aperture radar (ISAR) system with giant aperture antennas. With this technique, only a polarimetric active radar calibrator (PARC) with two independently rotary antennas is needed to calibrate the radar system. A complete polarimetric measurement mathematical model is adopted without any assumption. The calibration method is on the basis of Pauli matrix decomposition. We use four Lexicographic calibrators provided by the PARC instead of three Pauli calibrators. For these reasons, the calculation of error coefficients in the model is easier and the calibration performance is better than that in other calibration methods, such as the generalized calibration technique (GCT) and target spinor calibration (TSC). An experimental validation is presented. Excellent agreement is obtained between the theoretical analysis and the experimental results. A 35 dB improvement in the system cross-polarization isolation is obtained using the proposed method.

关键词： Polarimetric active radar calibrator Complete error model Matrix decomposition Lexicographic calibrators Cross-polarization isolation

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A reasoning method in conditional evidential networks based on DSm-PCR5 6

A reasoning method in conditional evidential networks based ...

引用

6th International Conference on Wireless, Mobile and Multi-Media, ICWMMN 2015

作者： Guo, Qiang Wang, Hai-Peng Li, Xian Smarandache, Florentin Xu, Shi-You Research Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai Shandong264001 China School of Information Science and Engineering Central South University Changsha Hunan410083 China Math. and Sciences Dept University of New Mexico 200 College Road GallupNM87301 United States Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781849199940

To solve the problem that the evidence information based on Dezert-Smarandache (DSm) model can not be effectively conditionally reasoned in multi-source heterogeneous network, a reasoning method in Conditional Evidential Network(ENC) based on DSm-Proportional Conflict Redistribution No.5(PCR5) is proposed. First, the conditional reasoning formula and the reasoning algorithm in Conditional Evidential Network based on DSm model is given. Then, the Disjunctive Rule of Combination(DRC) based on DSm-PCR5 is proposed and the Generalized Bayesian Theorem (GBT) for multiple intersection sets of focal elements is obtained in the premise that the conditional mass assignments functions of focal elements in refinement of hyper-power set is known. Finally, through the simulation experiments results of situation assessment, the effectiveness of the proposed method is ***: The authors are grateful to Prof. Zhun-ga Liu, Prof. Yong Deng, and Prof. Xin-de Li for discussions. We are also extremely grateful to all the anonymous reviewers for their critical and constructive review of the manuscript.

关键词： Heterogeneous networks

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Domain Adaptive SAR Ship Detection based on Knowledge Distillation and Transformer Attention

Domain Adaptive SAR Ship Detection based on Knowledge Distil...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Hong Tu Yue Guo Wei Wang Rubo Jin Shiqi Chen Zhongjie Wu National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology (NUDT) Changsha China College of Information and Communication National University of Defense Technology (NUDT) Wuhan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) ship detection has garnered considerable interest owing to its versatile application in both civilian and military field. Current SAR ship detectors based on Convolutional Neural Networks (CNNs) exhibit remarkable detection performance, contingent upon the availability of sufficient labeled SAR samples. However, it is time consuming and artificially expensive to label the training SAR images. Moreover, when the conditions of training data such as sensors, platforms, imaging parameters change, it is inevitable to alter the feature distribution between the source domain and the target domain, leading to the deterioration in detection performance. Consequently, the development of a detector capable of learning domain-invariant features without large-scale labeled samples in target domain remains a significant challenge. In this paper, we construct a semi-supervised domain-adaptive SAR ship detector based on YOLOv5, incorporating the Mean Teacher model with a knowledge distillation framework to capture domain-invariant features and mitigate domain discrepancies. Additionally, we integrate the transformer encoder module and convolutional block attention module into prediction head to enhance the capability of the detector to identify SAR ships of various scales across different domains. Extensive experiments conducted on several SAR ship datasets obtained under diverse conditions validate the effectiveness of our proposed approach.

关键词： YOLO Training Adaptation models Training data Detectors Feature extraction Transformers Radar polarimetry Marine vehicles Synthetic aperture radar

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