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Acceleration method for target HRRP generation based on DDPM

Acceleration method for target HRRP generation based on DDPM

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xitai Shen Qiang Zhou Bingqian Yu Xin Zhang Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity.

关键词： Training Target recognition Noise reduction Radar Radar scattering Data collection Feature extraction Sampling methods Diffusion models Vectors

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Space-surface Bistatic SAR Tomography: A Promising Approach for Accurate Local 3D Reconstruction

Space-surface Bistatic SAR Tomography: A Promising Approach ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Chen, Zhiyang Li, Yuanhao Chen, Xinyan Hu, Cheng Datcu, Mihai Monti-Guarnieri, Andrea Virgilio Hobbs, Stephen School of Information and Electronics Beijing Institute of Technology Beijing100084 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100084 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China CEOSpaceTech University Politehnica of Bucharest Bucharest050663 Romania The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom

ISBN: (纸本)9781839539954

With a flexibly deployed receiver, space-surface bistatic Synthetic Aperture Radar (SS-BiSAR) can increase the observation sensitivity in the north-south direction, which is a promising method for local high-precision three-dimensional deformation retrieval. However, in urban areas, the layover effect hinders the determination of the dominant scatterer in each resolution cell. Multi-baseline SAR tomography is an effective approach to solving overlaid scatterers. This paper presents the first experiment result of SS-BiSAR tomography. By estimating and removing the interferometric fringes, the repeat-track orbit errors are effectively corrected. The experiment was conducted based on the first L-band spaceborne twin SAR - the Chinese Lutan-1 (LT-1). A total of 11 repeat-track SAR images were acquired, and three-dimensional point clouds of a building area was recovered, with an accuracy of approximately 4 meters. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

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An improved method for rockfall detection and tracking based on video stream

An improved method for rockfall detection and tracking based...

引用

IET International Radar Conference (IRC 2023)

作者： Longyue Wang Songge Wang Xin Xie Yunkai Deng Weiming Tian Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing People's Republic of China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

Rockfall events occur frequently in mountainous areas. To address the problems of missed detection, false detection, and trajectory interruption when using the deep learning-based online multiple object tracking methods to detect rockfalls, this paper proposes a rockfall detection and tracking method based on video streams. In the detection stage, three-frame difference method is utilized to obtain the moving targets from the video streams, and they are combined with the detection results of the rock detector obtained by the offline-trained YOLOX model. In the tracking stage, data association is firstly performed based on the rockfall detection results. For the existing trajectories that are not matched at the current moment, re-matching is performed by combining the moving object detection results to achieve accurate tracking of rockfalls. Simulations and field experiments prove that the detection method proposed in this paper can effectively separate the rockfalls in the video, and the detected rockfalls have high precision. Besides, it significantly improves the accuracy of rockfall tracking, effectively suppressing phenomena such as trajectory interruption during tracking.

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

引用

9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Marine Ship Detection Method for SAR Image Based on Improved Faster RCNN

Marine Ship Detection Method for SAR Image Based on Improved...

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Chai, Bingqian Chen, Liang Shi, Hao He, Cheng Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665401234

Due to the multi-view imaging principle of synthetic aperture radar (SAR), its imaging process is not limited by any time or any bad weather. The detection of marine ship for SAR image is a very important application in both military and private applications. A marine ship detection method in SAR image about an improved Faster RCNN-based approach is proposed. First, the deepest semantic features of SAR image extracted by Faster RCNN contain less target information and small targets may be ignored, so that we fuse the deepest feature and shallower features in the feature extraction network;Secondly, because the redundant information in the features will produce false alarms, we embed the Convolutional Block Attention Module (CBAM) in the feature extraction network to extract more effective features;Finally, we use bilinear interpolation to obtain floating-point coordinates to optimize RoI pooling which uses rounding quantization, so the error mapping caused by quantization will be reduced. The algorithm of this paper is tested on the SSDD public dataset, and the AP is increased from 0.79 to 0.89. The results demonstrate that the algorithm introduced in this paper has a very significant performance improvement for detecting ship-like targets from SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

SAR Target Recognition Using Improved Monogenic-Based Featur...

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Feng Yao, Weijun Li, Yang Chen, Wei Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Beijing China School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Lab Beijing China

ISBN: (纸本)9781665498142

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance. © 2021 IEEE.

关键词： feature extraction monogenic signal small sample synthetic aperture radar (SAR) target recognition

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Radar HRRP Open Set Recognition Using Hierarchical Prototype Learning

Radar HRRP Open Set Recognition Using Hierarchical Prototype...

引用

IEEE International Conference on Radar

作者： Yichen Liu Yanhua Wang Liang Zhang Binhua Yu Xiaojing Yang Yu-ang Zhang Le Zheng Difan Zou School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profiles (HRRP) are increasingly employed in radar automatic target recognition (RATR). In practical applications of RATR, the environment is open and dynamic, and the recognition model is likely to encounter targets of unseen categories. However, traditional methods focus on a close-set scenario, where all categories in the test set are known during training. To bridge this gap, this paper proposes an open-set recognition (OSR) method based on hierarchical classification, developed from prototype learning. This method improves the generalization performance for recognizing known categories and identifies unknown categories while providing information about how the unknown categories relate to the known ones. Specifically, first, a four-layer category hierarchy is built based on prior knowledge to guide the training and testing stages. Next, we propose a hierarchical prototype loss (HPL) to constrain the feature space extracted by the prototype network so that the feature distribution of objects is consistent with the hierarchical category structure. Lastly, the trained prototype network makes predictions following the hierarchical structure. This operation could provide relationship information between the unknown and known categories. Extensive experiments on measured HRRP data validate the effectiveness of our proposed method for open-set recognition.

关键词： Training Bridges Target recognition Prototypes Radar Feature extraction Testing

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Generative Multi-View HRRP Recognition Based on Cascade Generation and Fusion Network

Generative Multi-View HRRP Recognition Based on Cascade Gene...

引用

IEEE International Conference on Radar

作者： Qiang Zhou Bingqian Yu Yanhua Wang Liang Zhang Le Zheng Difan Zou Xin Zhang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Department of Computer Science University of Hong Kong Hongkong China HKU Musketeers Foundation Institute of Data Science University of Hong Kong Hongkong China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

High-resolution range profile (HRRP) is critical for radar target recognition. However, HRRP data for non-cooperative targets are often sparsely collected, leading to limited performance of HRRP target recognition methods. To address the issue of sparse view target recognition, we propose a generative multi-view HRRP recognition (GMVR) model. Firstly, by leveraging the pattern learning and generation ability of deep generative models (DGMs), we propose a viewpoint feature expansion module (VFEM) to generate multi-view features adjacent to sparse viewpoints. Subsequently, based on feature-level fusion, we proposed a multi-view feature fusion module (MVFM). Aided by attention mechanisms, the generated features can be adaptively integrated, thereby reducing the decision bias caused by single-viewpoint HRRP samples. Additionally, we employ transfer learning to mitigate the small sample problem caused by sparse viewpoints. Experimental results demonstrate that the proposed method outperforms various comparison methods, achieving optimal performance.

关键词： Adaptation models Attention mechanisms Target recognition Transfer learning Radar Pattern recognition

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Weijia Wang Zhuchang Qi Lue Wang Wei Yang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi'an Institute of Modern Control Technology Xi'an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method.

关键词： Sensitivity Target recognition Convolution Fuses Autoencoders Dual band Scattering Radar Radar signal processing Signal resolution

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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wei Yang Zhuchang Qi Heke Wu Yang Li Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Xi'an Institute of Modern Control Technology Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets; we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule.

关键词： Sensitivity Limiting Target recognition Fuses Redundancy Dual band Radar Feature extraction Wavelet packets Frequency response

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