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MULTI-CHANNEL HBM PARALLEL STORAGE CONTROLLER FOR SPACEBORNE SAR IMAGING SYSTEM

MULTI-CHANNEL HBM PARALLEL STORAGE CONTROLLER FOR SPACEBORNE...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhang, Ao Zhou, Jiaping Li, Yongrui Zhang, Zhihan Xu, Ming Yang, Zhu Xie, Yizhuang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Electronics and Electrical Engineering Ningxia University Yinchuan750021 China Beijing Racobit Electronic Information Technology Co.Ltd. Beijing100081 China

ISBN: (纸本)9781839539954

With the continuous development of remote sensing technology, Spaceborne Synthetic Aperture Radar (SAR) is gradually evolving towards acquiring information in space and real-time on-board processing. Therefore, the real-time processing requirements for its imaging processing system are becoming increasingly stringent. The data bandwidth of the existing imaging processing systems cannot meet the high real-time processing demands. To address the aforementioned issues and satisfy the high bandwidth requirements of on-board SAR imaging processing systems, this paper proposes a multi-channel High Bandwidth Memory(HBM) parallel storage controller for Spaceborne SAR. It enables parallel data reading and writing for 16 data channels. The overall hardware design is based on the Virtex UltraScale+ HBM VCU128 FPGA platform. Through testing and verification, under the same clock frequency, the data bandwidth has been improved by approximately 10 times compared to existing solutions, achieving around 80GB/s of ultra-high bandwidth data read-write capability. Additionally, this design solution holds significant potential for further development, making it of considerable value and significance for the design of on-board SAR imaging processing systems. © The Institution of Engineering & technology 2023.

关键词： Integrated circuit design

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries 2

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Yu, Binhua Liu, Ping-An Liu, Yichen Wang, Hongyu Zhang, Liang Wang, Yanhua Liu, Kun Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target's pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method. © 2024 IEEE.

关键词： Radar imaging

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Hybrid Transformer Network for Change Detection Under Self-Supervised Pretraining

Hybrid Transformer Network for Change Detection Under Self-S...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yongjing Cui Yin Zhuang Shan Dong Xinyi Zhang Peng Gao He Chen Liang Chen Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Shanghai AI Laboratory Shanghai China

This paper presents a Siamese network architecture based on a multi-scale hybrid convolution-Transformer (CTUNet) for Change Detection (CD) in a pair of co-registered optical remote sensing images. Different form CD frameworks based on convolution neural networks (CNNs) and pure Transformer networks, this method combines a convolution-Transformer hybrid encoder with a multi-scale change information extraction decoder in a Siamese network architecture. It overcomes the inherent limitations of CNN and Transformer and effectively integrates the multi-scale information required for accurate CD. To learn better discriminative representations from various scales, we propose a masked auto-encoder scheme (CTMAE) to adapt to building targets with varying morphological scales, further unleashing the potential of CTUNet. Experiments on two CD datasets show that the proposed self-supervised pre-trained hybrid convolution-Transformer CTUNet architecture achieves better CD performance than previous methods.

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation 2

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Zhang, Zhilong Wu, Heke Xu, Yinhui Zhang, Liang Wang, Yanhua Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters. © 2024 IEEE.

关键词： Convolutional neural networks

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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm 2

Model Switching Detection-Aided Adaptive Transition Probabil...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Chen, Youwang Deng, Yunkai Tian, Weiming Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Institute of Technology Yangtze Delta Region Academy Zhejiang Jiaxing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology School of Information and Electronic Beijing China

ISBN: (纸本)9798331515669

In conventional interactive multiple model (IMM) algorithm, the fixed transition probability matrix results in slow model switching and low tracking accuracy. Existing adaptive transition probability IMM algorithms still suffer from large errors during model switching. This paper proposes a model switching detection-aided adaptive transition probability matrix IMM algorithm. To deal with model switch time delay, a transition probability correction function is established, capable of dynamically and rapidly adjusting the transition probabilities. And a model switching detection method is proposed to improve tracking accuracy during model switching. By the numerical simulation, the method's efficacy is substantiated and confirmed. © 2024 IEEE.

关键词： Adaptive Transition Probability Matrix IMM Model Switching Detection

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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method 2

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Meng, Yuanhao Wang, Lue Zhou, Qiang Zhang, Xin Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Modern Control Technology Research institute Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously. © 2024 IEEE.

关键词： Radar target recognition

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A network compression method based on network architecture search for SAR target detection

A network compression method based on network architecture s...

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IET International Radar Conference (IRC 2023)

作者： Penghe Zhao Zhengjie Jiang Baogui Qi He Chen Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China

ISBN: (数字)9781837240982

With the development of high-resolution satellites, real-time processing of satellite data has become a major development trend. However, due to the limitations of onboard resources, onboard real-time processing faces problems such as insufficient storage space, insufficient operating memory, and high computational complexity. To address the above issues, we propose a Synthetic Aperture Radar (SAR) image detection algorithm based on neural network structure search. By designing efficient and flexible search spaces, search strategies, and subnet evaluation modules, lightweight detection networks that meet specific task requirements can be searched from the original detection network. The proposed method was tested on three SAR ship datasets, and the results showed that the proposed method can reduce model complexity and improve inference speed while keeping detection accuracy nearly lossless.

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An Adaptive Phase Noise Filtering Approach for Multi-Frequency Interferometric SAR

An Adaptive Phase Noise Filtering Approach for Multi-Frequen...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wang, Zhen Ding, Zegang Wang, Yan Ma, Xinnong Li, Linghao Zeng, Tao Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology China

ISBN: (纸本)9781665498142

Multi-frequency (MF) interferometric SAR (InSAR) provides an effective way for the surveying of steep terrains. Phase noise filtering is a key step in MF-InSAR processing which determines the quality of the interferograms and affects the accuracy of inversed elevation. Different from the traditional filtering method of single frequency (SF) InSAR, an adaptive filtering approach for MF-InSAR based on MF phase fusion is proposed in this paper. The main contributions of this method is summarized into two aspects. First, the local fringe frequency estimation (LFFE) is extended from SF to MF case. Second, the non-linear model is introduced in the adaptive phase filtering in the local window based on MF-LFFE to adapt to complex terrains. The computer simulation and the real dual-frequency airborne experiment validate the proposed approach. © 2021 IEEE.

关键词： interferometric SAR local fringe frequency estimation multi-frequency phase noise filtering

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Lightweight CNN for HRRP Recognition Based on Attention Mechanism Structured Pruning

Lightweight CNN for HRRP Recognition Based on Attention Mech...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Wang, Yanhua Zhang, Zhilong Yuan, Mingchen Liao, Jiandong Zhang, Liang Beijing Institute of Technology Electromagnetic Sensing Research Center of Cemee State Key Lab Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Advanced Technology Research Institute Shandong250300 China

ISBN: (纸本)9781665482783

Deep convolutional neural network (CNN) has been widely investigated for radar target high resolution range profile (HRRP) recognition. However, the deep structures of CNN require high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we design a lightweight CNN model with structure pruning based on the channel-wise attention mechanism. Specifically, the attention value is used to represent the importance of the filters in CNN. Furthermore, Greedy strategy and fine-tuning are adopted in the pruning process to minimize the loss of model performance. The results of public dataset show that the recognition accuracy of the proposed method decreases by less than 0.13% when the pruning ratio is 80%. © 2023 IEEE.

关键词： Convolutional neural networks

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React Agent-Based Question Answering Method for Biology in National College Entrance Examination 24

React Agent-Based Question Answering Method for Biology in N...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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