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

共 293 条记录，以下是41-50 订阅

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Open Set target recognition Based on Multi-task Compressed Sensing using Multi-view High Range Resolution Profiles

Open Set Target Recognition Based on Multi-task Compressed S...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhang, Huiqiang Liu, Shengqi Teng, Shuhua Qu, Shuang Deng, Jie Yu, Zhenlin National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China College of Electronic Information Hunan First Normal University Changsha410205 China

ISBN: (纸本)9781839539954

This paper focuses on a specific application for a target recognition system. That is, the system input may be out-of-library from an unknown category and multiple observations can be obtained from a small angle interval. To efficiently utilize the multi-view samples for target identification in open-set situation, an open set recognition method using multi-view high range resolution profiles (HRRP) based on Multi-task Compressed Sensing (MtCS) is presented. First, the reconstruction errors of multi-view HRRPs on the over-completed dictionary are obtained by MtCS, and a modified metrics is constructed with the reconstruction error. Next, extreme value theory (EVT) is introduced to model the distribution of the metrics, and then the open-set recognition problem is transformed into a hypothesis test problem. The proposed method can effectively reject the out-of-library samples, and meanwhile exploit the prior information that the multiple observations are strongly correlated to improve the recognition performance of samples from known targets. The efficiency of the proposed method is identified through the recognition experiments on the HRRP data generated from MSTAR SAR chips. © The Institution of Engineering & technology 2023.

关键词： Compressed sensing

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

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

引用

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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A Lightweight SAR Ship Detection Method Based on Improved YOLOv8

A Lightweight SAR Ship Detection Method Based on Improved YO...

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IET International Radar Conference 2023, IRC 2023

作者： Guo, Yue Zhan, Ronghui Chen, Shiqi Li, Luzhuo Zhang, Jun 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: (纸本)9781839539954

The complex background and small size of targets pose challenges for ship detection in SAR (Synthetic Aperture Radar) images. Currently, deep learning-based methods have been widely used for SAR ship detection. However, the neural networks involved in these methods have a large number of parameters, which hinders their practical applications on resource-constrained platforms. This paper proposes a lightweight SAR detection algorithm specifically designed for small ship targets in SAR images. Firstly, we adopt a decoupling strategy to reconstruct the backbone network, effectively reducing the number of parameters. Secondly, a new regression loss function MPDIoU is proposed to enhance the detection accuracy of small ship bounding boxes and improve the regression speed of the model. Finally, we conduct experiments on the SSDD and HRSID datasets, and the results show that our proposed method achieves improvement in mAP (mean average precision) by 97.8% and 89.2% respectively, while the model weight parameters is only 72.5% of the standard YOLOv8n. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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MHRA-Net: Azimuth-Aware Multi-Head Residual Self-Attention Network for SAR Vehicle recognition

MHRA-Net: Azimuth-Aware Multi-Head Residual Self-Attention N...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Huiqiang Zhang Tianzhu Zhang Jie Deng Wei Wang Shengqi Liu Jun Zhang National Key Laboratory of Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Deep learning methods have made profound advancements in the field of synthetic aperture radar (SAR) target recognition. Typically, a significant amount of training data is required. However, due to the high degree of prior expert knowledge required for the annotation of SAR images, it is challenging to obtain a large amount of labeled data, which significantly impacts the performance of target recognition. To address this issue, this paper introduces an Azimuth-Aware Multi-Head Residual Self-Attention Network (MHRA-Net) that can extract high discriminative features of targets. Initially, this model employs a sub-aperture decomposition method to expand target information across multiple azimuth angles. Subsequently, we design a multi-head residual self-attention mechanism that can extract salient features of targets from multiple perspectives. Finally, a multi-scale feature fusion module is used to extract both global and local information about the target, enhancing model robustness and allowing the network to achieve satisfactory recognition performance even under sample-constrained conditions. Experimental results on a 10-class vehicle SAR image dataset demonstrate the effectiveness of the proposed approach.

关键词： Deep learning Image recognition target recognition Training data Geoscience and remote sensing Feature extraction Radar polarimetry

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Optimization Tracking Algorithm Based on Extended target Gaussian Mixture PHD Filter 22

Optimization Tracking Algorithm Based on Extended Target Gau...

引用

Proceedings of the 8th International Conference on Communication and Information Processing

作者： Liping Guo Xinglin Shen Shanzhu Xiao Huanzhang Lu Key Laboratory of Automatic Target Recognition National University of Defense Technology China

ISBN: (纸本)9781450397100

Under low signal-to-noise ratio (SNR) target tracking, poor target information and high clutter limit the tracking effect. Extended targets potentially generate more than one measurement per time step. Multiple extended targets tracking is therefore can be used to improve tracking performance with low SNR, due to the expanded data than point targets tracking. Based on the classical probability hypothesis density (PHD) filter, the extended target PHD (ET- PHD) filter is proposed to track multiple extended targets. The main contribution of this paper is the improvement of the classical extended target Gaussian-mixture probability hypothesis density (ET-GM-PHD) filter. A method based on the ET-GM-PHD filter is proposed for decreasing false alarms and improving measurement set partition performance under low SNR cases. The optimized method is shown a better tracking performance in estimation accuracy of the targets number and targets state in comparison with a point PHD filter.

关键词： measurement set partition low SNR extended targets tracking ET-GM-PHD filter

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Improved Sparse Representation of Open Set HRRP recognition Method

Improved Sparse Representation of Open Set HRRP Recognition ...

引用

Electronics and Communication, Network and Computer technology (ECNCT), International Conference on

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

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.

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Parallel Two-Stage Multiple Hypotheses Testing Model of Track-Before-Detect 5

Parallel Two-Stage Multiple Hypotheses Testing Model of Trac...

引用

5th International Conference on Pattern recognition and Artificial Intelligence, PRAI 2022

作者： Du, Jinming Lu, Huanzhang Zhang, Luping Hu, Moufa Shen, Xinglin Li, Dongyang National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China National University of Defense Technology College of Military Basic Education Changsha China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Multiple hypotheses testing (MHT)-based Track-before-Detect (TBD) method is a widely known algorithm to detect infrared dim small target. However, the hypotheses space in this kind of method is so big that the computation cost and storage requirement inevitably increase, resulting hardly realized in hardware. To solve the problem, a parallel two stage multiple hypotheses testing model is proposed. First, a parallel MHT model is designed to save the possible trajectories. By using the proposed MHT, all trajectories can be parallelly processed and the trajectories search process is simplified, greatly reducing computation cost. Secondly, for each pixel on the current image, the trajectories search range is divided into 4 different areas, and two-stage MHT is performed in each area to obtain the accumulation energy. By using two-stage strategy, the trajectories number is reduced exponentially, greatly saving storage space. Simulations experimental results show the superiority of the proposed parallel two stage MHT in terms of detection probability and computational complexity under low signal-to-noise ratio (SNR) condition. It can detect the infrared point target when SNR is 1.5 with a detection probability of 90.6% and a false alarm rate of 0.1%. © 2022 IEEE.

关键词： Trajectories

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Radar active jamming recognition based on time-frequency image classification 21

Radar active jamming recognition based on time-frequency ima...

引用

5th International Conference on Electronic Information technology and Computer Engineering, EITCE 2021

作者： Wang, Jingyi Dong, Wenhao Song, Zhiyong National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha China

ISBN: (纸本)9781450384322

Flexible active radar jamming has become one of the main threats in modern electronic warfare. Aiming at the problem of classifying and identifying active jamming patterns of towed decoy radar, this paper proposes a set of methods for classifying and identifying jamming time-frequency images based on deep learning. The content mainly includes: using short-time Fourier transform to obtain time-frequency images of six kinds of active interference under different interference signal ratios. Using ResNet network to realize the classification and recognition of different interferences. Through simulation analysis, the algorithm can still obtain better results under low interference-to-noise ratio. © 2021 ACM.

关键词： Jamming

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Design of radar waveform game platform based on PXI system 10

Design of radar waveform game platform based on PXI system

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10th International Conference on Control, Automation and Information sciences, ICCAIS 2021

作者： Wang, Min Jiang, Yanwen Fan, Hongqi National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition ChangSha China

ISBN: (纸本)9781665440295

In view of the lack of a test verification platform in the research of the game between radar and target, a test platform for radar waveform game is designed based on the PXI system. The platform includes a multi-channel radar and a simulator that can simulate targets and repeater jamming. The radar transmission waveform, signal processing, target and jamming parameters can be configured by the user, and it has good versatility. It provides a practical test and verification platform for radar waveform design and algorithm research under gaming conditions. © 2021 IEEE.

关键词： Radar

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