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GS-orthogonalization OMP method for space target detection via bistatic space-based radar

Chinese Journal of Aeronautics 2024年第7期37卷 333-351页

作者： Shuyu ZHENG Libing JIANG Qingwei YANG Yingjian ZHAO Zhuang WANG National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and TechnologyNational University of Defense TechnologyChangsha 410073China

A space-based bistatic radar system composed of two space-based radars as the transmitter and the receiver respectively has a wider surveillance region and a better early warning capability for high-speed targets,and it can detect focused space targets more flexibly than the monostatic radar system or the ground-based radar ***,the target echo signal is more difficult to process due to the high-speed motion of both space-based radars and space *** be specific,it will encounter the problems of Range Cell Migration(RCM)and Doppler Frequency Migration(DFM),which degrade the long-time coherent integration performance for target detection and localization *** solve this problem,a novel target detection method based on an improved Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm is proposed in this ***,the echo model for bistatic space-based radar is constructed and the conditions for RCM and DFM are ***,the proposed GS-orthogonalization OMP method is applied to estimate the equivalent motion parameters of space ***,the RCM and DFM are corrected by the compensation function correlated with the estimated motion ***,coherent integration can be achieved by performing the Fast Fourier Transform(FFT)operation along the slow time direction on compensated echo *** simulations and real raw data results validate that the proposed GS-orthogonalization OMP algorithm achieves better motion parameter estimation performance and higher detection probability for space targets detection.

关键词： Bistatic space-based radar High-speed maneuvering space targets detection Range Cell Migration(RCM) Doppler Frequency Migration(DFM) Gram Schmidt(GS)-orthogonalization Orthogonal Matching Pursuit(OMP)algorithm

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Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1174-1189页

作者： Wei Xue Xiaoyan Xia Pengcheng Wan Ping Zhong Xiao Zheng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense TechnologyChangsha 410073China

Deep neural networks are commonly used in computer vision tasks,but they are vulnerable to adversarial samples,resulting in poor recognition *** traditional algorithms that craft adversarial samples have been effective in attacking classification models,the attacking performance degrades when facing object detection models with more complex *** address this issue better,in this paper we first analyze the mechanism of multi-scale feature extraction of object detection models,and then by constructing the object feature-wise attention module and the perturbation extraction module,a novel adversarial sample generation algorithm for attacking detection models is ***,in the first module,based on the multi-scale feature map,we reduce the range of perturbation and improve the stealthiness of adversarial samples by computing the noise distribution in the object *** in the second module,we feed the noise distribution into the generative adversarial networks to generate adversarial perturbation with strong attack *** doing so,the proposed approach possesses the ability to better confuse the judgment of detection *** carried out on the DroneVehicle dataset show that our method is computationally efficient and works well in attacking detection models measured by qualitative analysis and quantitative analysis.

关键词： adversarial attack transfer attack object detection generative adversarial networks multi-scale feature map

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Staring-imaging satellite pointing estimation based on sequential ISAR images

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Chinese Journal of Aeronautics 2024年第8期37卷 261-276页

作者： Canyu WANG Libing JIANG Weijun ZHONG Xiaoyuan REN Zhuang WANG National Key Laboratory of Automatic Target Recognition National University of Defense TechnologyChangsha 410073China Xi’an Satellite Control Center Xi’an 710600China

Pointing estimation for spacecraft using Inverse Synthetic Aperture Radar(ISAR)images plays a significant role in space situational awareness and ***,feature extraction and cross-range scaling of ISAR images create bottlenecks that limit performances of current estimation ***,the emergence of staring imaging satellites,characterized by complex kinematic behaviors,presents a novel challenge to this *** address these issues,this article proposes a pointing estimation method based on Convolutional Neural Networks(CNNs)and a numerical optimization algorithm.A satellite’s main axis,which is extracted from ISAR images by a proposed Semantic Axis Region Regression Net(SARRN),is chosen for investigation in this article due to its unique ***,considering the kinematic characteristic of the staring satellite,an ISAR imaging model is established to bridge the target pointing and the extracted *** on the imaging model,pointing estimation and cross-range scaling can be described as a maximum likelihood estimation problem,and an iterative optimization algorithm modified by using the strategy of random sampling-consistency check and weighted least squares is proposed to solve this ***,the pointing of targets and the cross-range scaling factors of ISAR images are *** experiments based on actual satellite orbital parameters verify the effectiveness of the proposed *** work can improve the performance of satellite reconnaissance warning,while accurate cross-range scaling can provide a basis for subsequent data processes such as 3D reconstruction and attitude estimation.

关键词： Staring-imaging Agile satellite Inverse Synthetic Aperture Radar(ISAR) Pointing estimation Cross-range scaling

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Reflection separation technology based on polarization characteristics

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Journal of Systems Engineering and Electronics 2022年第5期33卷 1032-1042页

作者： ZHANG Yan ZHANG Jinghua SHI Zhiguang ZHANG Yu LING Feng National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and EngineeringNational University of Defense TechnologyChangsha 410073China

Specific to the reflected light problem on the surface of transparent body,the polarization characteristics of the reflection region are analyzed,and a polarization characterization model combining the reflection and transmission effects is *** the basis of the polarization characteristic analysis,the minimum value of normalized cross-correlation(NCC)coefficient between transmission and reflection images is solved through the gradient descent method,and their polarization degrees under the minimum correlation are *** to the distribution relations of the transmitted and reflected lights in perpendicular and parallel directions,reflection separation is realized via the polarized orthogonality difference algorithm based on the degree of reflection polarization and the degree of transmission polarization.

关键词： reflection separation transparent object correlation polarization characteristics

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A modified OMP method for multi-orbit three dimensional ISAR imaging of the space target

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Journal of Systems Engineering and Electronics 2023年第4期34卷 879-893页

作者： JIANG Libing ZHENG Shuyu YANG Qingwei YANG Peng WANG Zhuang Science and Technology on Automatic Target Recognition National University of Defense TechnologyChangsha410073China Xi’an Satellite Measurement and Control Center Xi’an 710600China

The conventional two dimensional(2D)inverse synthetic aperture radar(ISAR)imaging fails to provide the targets'three dimensional(3D)*** this paper,a 3D ISAR imaging method for the space target is proposed based on mutliorbit observation data and an improved orthogonal matching pursuit(OMP)***,the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction ***,an improved OMP algorithm is applied to recover the space target's amplitude information via the 2D matrix ***,scattering centers can be reconstructed with specific three dimensional *** simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.

关键词： three dimensional inverse synthetic aperture radar(3D ISAR)imaging space target improved orthogonal matching pursuit(OMP)algorithm scattering centers

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Narrow-beam radar clutter distortion correction method based on clutter pre-screening and beam pattern demodulation 6

Narrow-beam radar clutter distortion correction method based...

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6th International Conference on Information science, Electrical, and Automation Engineering, ISEAE 2024

作者： Xu, Yong Zhu, Yongfeng Song, Zhiyong National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9781510682924

For a narrow-beam radar working in a down-looking view, the antenna pattern modulates the ground clutter apparently in the delay/range dimension, causing amplitude/intensity distortion of radar echoes and performance loss of target detection. This paper proposes a range-dimensional clutter distortion correction method based on clutter pre-screening and beam pattern demodulation. This method realizes the correction of clutter distortion, to improve the target detection performance of Narrow-beam radar. The effectiveness of the method is verified through simulations and experimental measurements. The pro-posed method demonstrates practical significance in improving the detection probability of radar targets under clutter distortion in narrow-beam broadband radar systems. © 2024 SPIE.

关键词： Radar clutter

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Two-stage small target detection algorithm based on feature fusion 4

Two-stage small target detection algorithm based on feature ...

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4th International Conference on Computer Vision and Data Mining, ICCVDM 2023

作者： Li, Wei Zhang, Zhilong Li, Chuwei National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510674448

To solve the problem that the traditional general algorithm can not detect small targets well, this paper adds a new feature fusion branch and introduces an attention mechanism on the basis of yolov4 algorithm, which avoids feature aliasing caused by simple fusion and enhances the expression ability of the network for small targets. Compared to the original network, mAP improved by 2.2%. On this basis, considering that the low confidence prediction box also contains the prediction information of small targets, but the traditional method directly rejects it rudely, resulting in missing detection. In this paper, the K-means clustering algorithm is used to analyze the size distribution characteristics of the targets in the data set, then the image is divided into regions, the region containing the low confidence prediction frame is enlarged by super resolution, refined detection, the results are mapped back to the original location, and the results are uniformly processed by NMS. In the end, compared with the original network, mAP improves by 4.4%, and the network's recall rate of small targets has been significantly improved by nearly 8%, which can effectively improve the performance of small target detection. © 2024 SPIE.

关键词： Forecasting

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A Hyperspectral Nonlinear Unmixing Network for Nearshore Underwater target Detection 5

A Hyperspectral Nonlinear Unmixing Network for Nearshore Und...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

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

ISBN: (纸本)9798350385557

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. © 2024 IEEE.

关键词： Learning systems

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Improved Sparse Representation of Open Set HRRP recognition Method 5

Improved Sparse Representation of Open Set HRRP Recognition ...

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5th International Conference on Electronics and Communication, Network and Computer technology, ECNCT 2023

作者： Zhang, Huiqiang Liu, Shengqi Qu, Shuang Ou, Jianping Zhang, Jun Deng, Jie National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China

ISBN: (纸本)9798350328660

Traditional target recognition methods classify targets within a closed set, and the methods cannot effectively identify unknown categories outside the library. Research on open set recognition (OSR) has been developed in computer vision, yet rare research is conducted in radar target recognition. First the HRRP recognition performance of several state-of-The-Art OSR algorithms are compared in open set, including SROSR, WSVM, and 1-vs Set. Among them the SROSR algorithm performances better than the others. However, manual setting of tail and weight parameters is required in the algorithm, which may difficult to be obtained in practical applications. Focused on this issue, an modified SROSR method based on the mean ratio of reconstruction errors is presented. The mean ratio of reconstruction errors is used as a new metrics and its statistical distribution is modeled with extreme value theory (EVT). Compared with the classical SR-OSR, manual setting of the tail of reconstruction errors and weight parameters are avoided. Experiments were conducted on HRRP data generated from MSTAR SAR chips, and the results show that the recognition performance of the proposed method is better than the classical SROSR algorithm. © 2023 IEEE.

关键词： Radar target recognition

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Domain Adaptive SAR Ship Detection based on Knowledge Distillation and Transformer Attention 2

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

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

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Convolutional neural networks

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