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Tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline 2

Tomographic imaging method of GB-ArcSAR with arc-shaped inte...

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

作者： Zhou, Hanpu Yan, Kai Wang, Jian Li, Xiaoxiao Tian, Weiming Deng, Yunkai Bejing Institute of Technology Radar Technology Research Institute Beijing China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Building Research Institute Corporation Limited Cscec Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (纸本)9798331515669

When observing building scenarios, there exists the layover problem. Synthetic aperture radar (SAR) tomography can use multiple two-dimensional (2D) SAR images acquired from different heights to inverse the range-azimuth-height 3D information of the targets. Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can obtain a series of SAR images at different heights by performing azimuthal scanning on horizontal planes with different pitch angles, which makes it has tomography capabilities. However, the baseline of GB-ArcSAR is arc-shaped, which makes the existing tomographic imaging methods suitable for linear tomographic baselines cannot be directly applied to GB-ArcSAR. This paper proposes a tomographic imaging method of GB-ArcSAR with arc-shaped interferometric baseline. Through Taylor expansion of the distance between the target and the sampling position, a mapping relationship between the target pitch angle and interferometric phase was established. Based on the mapping relationship, the measurement matrix was constructed. Validation of the proposed method is achieved through simulations and measured data. © 2024 IEEE.

关键词： Radar imaging

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A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATURES IN ENTOMOLOGICAL RADAR

A TRACK ASSOCIATION ALGORITHM BASED ON MOTION DYNAMICS FEATU...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Biao Liu, Hanzhe Zhang, Tianran Cai, Jiong Wang, Rui The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

When tracking densely distributed targets such as insects, the traditional trajectory association algorithm exhibits poor correlation performance, leading to a decline in multi-target tracking efficiency. In this paper, based on the behavioral characteristics of insects gathering and co-orienting during aerial flight, a motion dynamic feature-assisted multi-target tracking method is proposed. Firstly, a three-dimensional biological distribution state matrix is constructed through spatial gridization. Then, a three-dimensional circular convolution is employed to estimate the target's motion dynamic feature, assisting in multi-target trajectory initialization. Finally, the improvement effect of the method on trajectory tracking accuracy is validated through simulations and migration insect data. © The Institution of Engineering & technology 2023.

关键词： Radar tracking

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Lightweight NPU Method Based on Hyper-Threading technology

Lightweight NPU Method Based on Hyper-Threading Technology

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

作者： Ao Zhang Yongrui Li Wenlong Li Yizhuang Xie Zhihan Zhang Zhu Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Racobit Electronic Information Technology Co. Ltd Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Racobit Electronic Information Technology Co.Ltd

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the rapid development of deep learning, the processing power of Neural processing Unit(NPU) continues to improve. However, this leads to an increasing scale of logical resources for NPU, making it challenging to deploy NPU on Field-Programmable Gate Array(FPGA). To address this issue and enable the deployment of high-computational-power NPU on FPGA, this paper proposes a lightweight NPU method based on hyper-threading technology, considering the characteristics of NPU hardware architecture. This method effectively reduces the logical resources usage of the NPU while ensuring only a minimal decrease in computational power, thus enabling successful FPGA deployment. The overall experiment is based on the Virtex UltraScale+ HBM VCU128 FPGA platform. After testing, it was found that the NPU, previously undeployable, could be successfully deployed after lightweight processing. The resource usage ratio of LUTs decreased by about 10%, and the computational power only decreased by 4%. In other words, by using this method, the scale of logical resource usage of the NPU was effectively reduced while ensuring a minimal decrease in computational power, improving the deployment situation of the NPU on FPGA. This approach has certain reference value and significance for the lightweight design of NPU.

关键词： Deep learning Computer architecture Logic gates Data processing Hardware Table lookup Field programmable gate arrays Signal resolution Logic arrays Testing

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Open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity 2

Open Set Radar HRRP Recognition Using Confidence through Neu...

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

作者： Liu, Yichen Liu, Ping-An Zhang, Liang Wang, Yanhua Bao, Zengdi Xu, Yinhui Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Xi'an Modern Control Technology Research Institute Xi'an710065 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China

ISBN: (纸本)9798331515669

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions. © 2024 IEEE.

关键词： Radar target recognition

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Exploring the potential of ultra-low latency spiking neural network in remote sensing image classification

Exploring the potential of ultra-low latency spiking neural ...

引用

IET International Radar Conference (IRC 2023)

作者： Jiahao Li Ming Xu He Chen Can Li Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

With the rapid advancement of artificial intelligence, the depth of artificial neural networks (ANN) has increased, resulting in higher power consumption and resource utilization. To address these concerns, researchers have begun to explore spiking neural networks (SNN) as an alternative. SNN offers the potential for improved energy efficiency while achieving comparable performance. In recent years, there has been a growing interest in applying SNN to remote sensing classification scenarios. However, one major challenge hindering its widespread adoption is the high latency associated with real-time processing. To address this challenge, we adopt a novel hybrid training approach that combines ANN-SNN conversion with backpropagation through time. We evaluate the effectiveness of our approach on three publicly available datasets: MSTAR, AID and UC-Merced. Our experiment results demonstrate that the proposed ultra-low latency SNN achieves classification accuracy rates of 99.05%, 91.36%, and 92.62%, respectively, with a single time step. To the best of our knowledge, this is the first time to apply ultra-low latency SNN to remote sensing image classification tasks. Our findings suggest that this innovative approach has significant potential for future research in the deployment of these models for on-board processing.

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

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

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

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

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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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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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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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