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IET International Radar Conference 2015

作者： Dinghe Wang Qinglong Bao Zhaodong Niu Zengping Chen Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha P. R. China

ISBN: (纸本)9781510822023

At present, most of the passive radar system researches utilize FM radios, TV broadcasts, navigation satellites etc. as illuminators. The transmitted signals are not specifically designed radar waveforms. In this work, a frequency agile, phased array Air Surveillance Radar (ASR) is used as illuminator of opportunity for weak target detection. In this case, coherent integration methods are not applicable. Thus, a parametric non-coherent integration algorithm based on task de-interweaving is proposed. Numerical experiments verify that this method is effective in weak target detection.

关键词： Air Surveillance Radar bistatic non-cooperative task de-interweave

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Vehicle Detection in High-Resolution Polsar Images Via GP-PNF Distribution Modeling

Vehicle Detection in High-Resolution Polsar Images Via GP-PN...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jie Deng Wei Wang Huiqiang Zhang Sinong Quan Jun Zhang National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Vehicle detection is an important application of polarimetric synthetic aperture radar (PolSAR). Geometrical perturbation polarimetric notch filter (GP-PNF) establishes a feature space based on the local background polarimetric characteristics to achieve adaptive detection of ship targets. However, the complexity of the ground background presents additional challenges compared to sea surface. In this work we model the distribution of the GP-PNF and prove its effectiveness and accuracy compared with other common distribution models based on real airborne mini-SAR data. And then we introduce a numerical calculation of logarithm cumulants for parameters estimation, derive the constant false alarm rate (CFAR) threshold computation formula and apply the filter to vehicle detection. Experiments performed on real high-resolution PolSAR images verify the good performance of the detection method.

关键词： Adaptation models Sea surface Parameter estimation Vehicle detection Computational modeling Atmospheric modeling Perturbation methods

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Random Projection Recurrent Neural Networks for Time Series Classification

Random Projection Recurrent Neural Networks for Time Series ...

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International Conference on Electronics Information and Emergency Communication

作者： Ye Ma Moufa Hu Qing Chang Huanzhang Lu College of Electronic Science National University of Defense Technology Laboratory of Automatic Target Recognition Changsha Hunan Province China

Recurrent neural networks (RNNs) remain challenging and keep lack of a long-term memory or learning ability in sequential data classification and prediction. In this paper, we propose an ingenious recurrent model, Random projection RNNs (R-RNNs), incorporating random projection to advance the classification accuracy limited by the gradient vanishing and exploding problem before. Experiments on public time series datasets demonstrate that our proposed method outperforms several existing typical models substantially. And the efficiency of R-RNNs concerning SNR is also discussed.

关键词： Time series analysis Training Feature extraction Recurrent neural networks Signal to noise ratio Neurons Task analysis

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A Hyperspectral Nonlinear Unmixing Network for Nearshore Underwater target Detection

A Hyperspectral Nonlinear Unmixing Network for Nearshore Und...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Jiaxuan Liu Jiahao Qi Ping Zhong Junhe Jiang Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China Science and Technology on Near-Surface Detection Laboratory Wuxi China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

The existing hyperspectral underwater target detection algorithms are ineffective in adapting to nearshore environments, owing to the difficulty of extracting target signals from cluttered nearshore backgrounds combined with weak target signals. To this end, hyperspectral unmixing (HU) is well suited in this paper for nearshore underwater target detection (UTD) task to address these issues, by extracting background endmembers and separating out weak target signals simultaneously. Specifically, we propose a novel nonlinear unmixing network for nearshore underwater target detection (NUN-UTD). It comprises an unsymmetrical autoencoder and a target prior preservation module to adapt to the complex nearshore environment. Experiments demonstrate that NUN-UTD achieves the best detection performances and visual results compared with other state-of-the-art methods.

关键词： Seminars Visualization Object detection Task analysis Information technology Artificial intelligence

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A New ISAR Range Alignment Method via Minimum Tsallis Entropy

A New ISAR Range Alignment Method via Minimum Tsallis Entrop...

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2013 IEEE International Conference on Microwave technology & Computational Electromagnetics

作者： HU Moufa GAO Lei ZHANG Zhiyong The State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics&Information System Science and Technology on Automatic Target Recognition(ATR) NUDT

ISBN: (纸本)9781467363266

Motion compensation is very important for Inverse synthetic aperture radar(ISAR) imaging while the imaging quality is directly dependent on its *** alignment(envelope alignment) is the key procedure of motion *** nonextensive entropy,known as Tsallis entropy,is introduced into ISAR range alignment because of the correlation between adjacent onedimensional range profile(or long time memory characteristic).Firstly,the principle of range alignment method via minimum Shannon entropy(RAMSE) was *** on this,a new adjacent range profile alignment method was given via minimum Tsallis entropy(RAMTE).Further,in order to reduce the accumulation of alignment error and jump error,the modified RAMTE(mRAMTE) was presented using accumulation range profile alignment and smooth *** on the criterion of minimum Tsallis entropy,mRAMTE could reduce the alignment error into 1/8 range resolution cell by data *** results and experiments show that mRAMTE can effectively compensate range walk caused by target *** it was also shown that the alignment performance of mRAMTE is better than RAMSE.

关键词： Inverse Synthetic Aperture Radar(ISAR) imaging.Minimum entropy.Range Alignment.Shannon entropy.Tsallis entropy.

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A Wide-Angle Scanning Focal Plane Array Design Method for Millimeter-Wave Applications

A Wide-Angle Scanning Focal Plane Array Design Method for Mi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hongfei Lian Huaitie Xiao Hongqi Fan Yanwen Jiang National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This article investigates the limits of focal plane array (FPA) systems with wide-angle scan capabilities. A design and analysis methodology has been established to optimize the system. A physical optics (PO) model is used to obtain focal plane fields. The influence of focal length over dish diameter ratios (F/D) of the antenna on the focusing characteristics is analyzed. In addition, the impact of axial displacements of the FPA on aperture efficiency is presented. The study gives guidance for the optimal F/D and the axial displacements to improve scan capabilities for a given focal plane array size.

关键词： Radar remote sensing Design methodology Simulation Physical optics Geoscience and remote sensing Radar imaging Millimeter wave radar

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The hybrid law design based on interacting multiple model algorithm

The hybrid law design based on interacting multiple model al...

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International Conference on Measurement, Information and Control (MIC)

作者： Xuan-li Fu Shi-jie Fan Hong-qi Fan Huai-tie Xiao Science and Technology on Automatic Target Recognition Laboratory National University of Defense Technology Changsha Hunan P. R. China

A hybrid differential game law DGL/H based on IMM algorithm is proposed for endgame randomly maneuvering target interception to solve the robustness of traditional single guidance law and the target state estimation delayed high sensitivity. IMM/DGL/H law takes the performance advantage of three differential game laws, DGL/C, DGL/1 and DGL/0 law, into account, employs the change of model probability of IMM algorithm to detect the evasive maneuver, and then achieves the guidance law online options in use of the detection time to weaken the estimation delay for homing performance. Simulation results show that IMM algorithm for acceleration estimation can obtain higher estimation accuracy. The hybrid differential game law DGL/ H, compared with a single guidance law, has a better homing performance.

关键词： Sensitivity Radio frequency Acceleration

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Digital image forgeries detection based on blocking artifact

Digital image forgeries detection based on blocking artifact

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2010 IEEE Youth Conference on Information, Computing and Telecommunications, YC-ICT 2010

作者： Zhongmei, Wang Yonghong, Long Department of Computer and Communication Hunan University of Technology College Zhuzhou China National Laboratory of Pattern Recognition Institute of Automatic Chinese Academy of Science Beijing China

ISBN: (纸本)9781424488841

Digital image forgery detection becomes an important research topic now in digital image forensics. JPEG as a popular file form for digital image. DCT transformation and quantization are two main parts of JPEG encoding process. Quantization table is used for quantizing the DCT coefficient and have been proved as an effective feature for image forgery detection. In this paper, we describe an approach to detect digital forgeries by checking inconsistencies of JPEG blocks and measure the block quantization error to get the forgery area. Experiment results also demonstrate the validity of the proposed approach. © 2010 IEEE.

关键词： Digital forensics

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Multitone Waveform Design towards Enhancing Conversion Efficiency for RF-electronics Detection

TechRxiv

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TechRxiv 2023年

作者： Li, Yuanzhe Hu, Weidong Fan, Hongqi Du, Xiaoyong Fan, Beichen National Key Laboratory of Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

Nonlinear radar has attracted considerable attention in recent years due to its unique advantage in detecting nonlinear targets, such as Radio-Frequency (RF) electronics, under strong clutter conditions. However, the low nonlinear RF-to-RF conversion efficiency (RRCE) severely limits its operational range and application scenarios. So far, the research community has mainly focused on improving the RRCE from the hardware aspects while ignoring the signal design. In this paper, we study the waveform design problem that maximizes RRCE for detecting nonlinear targets. Assuming perfect channel state information and target characteristics, we develop a tractable block-oriented nonlinear model and formulate the waveform design as a constrained optimization problem. Then two iterative algorithms based on successive relaxation are proposed to solve the non-convex problem for different cases. Besides, the simulations and laboratory experiments demonstrate the effectiveness of the system model and the superiority of the proposed waveform design method over various baselines. More than 4 dB improvement in RRCE is observed for the given configurations in the experiment. The study provides a promising alternative for extending the maximum distance of nonlinear radar. © 2023, CC BY.

关键词： Iterative methods

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SFD-YOLO: A novel framework for subsidence funnels detection in China based on large-scale SAR interferograms

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International Journal of Applied Earth Observation and Geoinformation 2025年 140卷

作者： Jing Guo Zhengjia Zhang Peifeng Ma Mengmeng Wang Xuefei Zhang Dongdong Li Bing Sui School of Geography and Information Engineering China University of Geosciences 388 Lumo Road Wuhan 430074 China Department of Geography and Resource Management The Chinese University of Hong Kong Shatin Hong Kong Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen 518063 China Land Satellite Remote Sensing Application Center MNR Beijing China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction Changsha China

Accurate identification of subsidence funnels is essential for assessing surface deformation in mining areas, preventing disasters, and optimizing resource management. However, recognizing subsidence funnels of varying sizes in large-scale interferograms poses significant challenges, particularly for small-sized funnels. Their indistinct features and susceptibility to background noise interference often result in suboptimal detection accuracy. To address these challenges, this study proposes a deep learning network based on the YOLO architecture—SFD-YOLO (Sinking Funnel Detection-YOLO). The model incorporates the DWR-C2f module, which enhances multi-scale feature extraction and significantly improves the detection of small-sized subsidence funnels. Additionally, the innovative Inner-WIoU regression loss function improves the localization accuracy of detection boxes while also alleviates the imbalance between hard and easy samples. Experimental results demonstrate that the fully trained SFD-YOLO model achieves an mAP50 accuracy of 92.00% while maintaining high efficiency, significantly outperforming other advanced methods. Applying the SFD-YOLO model to interferograms across China detected a total of 3,842 subsidence funnels, with Shanxi, Inner Mongolia, Shaanxi, and Anhui identified as the four provinces with the highest funnel number. Overall, subsidence funnels are predominantly distributed in northern and northwestern China. Further analysis and experimental evaluation reveal that the SFD-YOLO model exhibits strong generalization capabilities across complex surface environments nationwide and multi-source satellite data.

关键词： Mining subsidence detection National scale Interferograms Deep learning Small-sized funnels

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