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检索条件"机构=Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology"

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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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A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-MIMO RADAR WITH COMBINED DOUBLE/MULTI-PULSE CHANNELS

A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-M...

引用

IET International Radar Conference 2023, IRC 2023

作者： Ren, Xiaoyu Qi, Jingshuang Deng, Yunkai Lin, Ruiqi Chen, Youwang Tian, Weiming Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

MIMO(Multiple-Input Multiple-Output) radar can be effectively applied to the real-time detection of rockfalls, but it is challenging to balance the measurement range of unfuzzy velocity and the real-time detection performance when using TDM(time Division Multiplexing) waveform. Besides, the estimation accuracy of DOA(Direction-Of-Arrival) of rockfalls is easily disturbed by the motion phase. Aiming at solving the above problems, a rockfall motion parameter estimation method based on TDM-MIMO radar with combined double/multi-pulse channels is proposed in this paper. Firstly, by designing the time sequence of TDM signals, the problem of mutual limitation between the detection range of velocity and real-time detection performance is solved. Secondly, the problem of inaccurate angle estimation caused by Doppler frequency is solved. When there is only one target in the range unit, the motion phase should be compensated first. When multiple targets are located in the same range unit, the velocity of these targets is used to judge the corresponding azimuth, and then velocity-azimuth decoupling is completed. Finally, the effectiveness of the proposed method is verified by simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Rock bursts

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Lightweight CNN for HRRP Recognition Based on Attention Mechanism Structured Pruning

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

引用

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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HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL NETWORK

HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL N...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhang, Yu-Ang Zhou, Na Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory 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 Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

Scattering center estimation of HRRP is crucial for radar automatic target recognition (RATR). Traditional estimation algorithms either necessitate prior information of the signal or suffer from mismatch issues or possess excessive computational costs. In this paper, we propose an HRRP-modelled neural network (HNN), which addresses the mismatch problem and exhibits better accuracy. HNN combines the HRRP signal model with its layered structure, including input, output and activation function. It is initialized via orthogonal matching pursuit and optimized using back propagation algorithm with dual learning rate. The estimated position and amplitude can be obtained by the weights between layers. Through simulations and experiments, we demonstrate the superiority of HNN over traditional methods. © The Institution of Engineering & technology 2023.

关键词： Backpropagation

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RADAR TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

RADAR TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

引用

IET International Radar Conference 2023, IRC 2023

作者： Liu, Jiaqi Yuan, Mingchen Zhang, Liang Wang, Yanhua School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory 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 Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High-resolution range profile (HRRP) formed by the extended target under high-resolution radar can reflect the detailed information of the target, which is one of the important means of target detection for air-to-ground radar. Accurately segmenting the target HRRP from the entire echo is of great significance for studying the target characteristics, target feature extraction and recognition. In this paper, a radar target HRRP segmentation algorithm using diffusion model is proposed. The disturbed position parameters of the target HRRP are generated according to the idea of adding noise by the diffusion model, and then the predicted segmentation results are obtained through the trained model to modify the disturbed parameters. The performance of the algorithm under different signal-to-noise ratios and polarization modes is evaluated in a measured dataset. The experimental results show that this method can effectively obtain target HRRPs and can be applied to the extended target segmentation of high-resolution radar. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM MINIMIZATION

DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Na Zhang, Yu-Ang Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory 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 Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High resolution range profile (HRRP) obtained through air-to-ground radar provides information on the target structure, which is widely used in military and aviation fields. On the other side, dual band radar is able to improve the performance of target detection and recognition by fusing the information of different band. In this paper, we propose a dual band HRRP scattering center estimation method based on atomic norm minimization (ANM). Due to the fact that dual band information reflects the characteristics of the same target, it is modelled as the multiple measurement vector (MMV) and incorporated into the modelling process to effectively utilize the correlation of dual band information and sparse reconstruction. This method estimates in a continuous domain, avoiding the problem of base mismatch. According to the experimental results, this method exhibits outstanding advantages in accuracy and robustness. © The Institution of Engineering & technology 2023.

关键词： Radar target recognition

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Radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

引用

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

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China Electromagnetic Sensing Research Center of Cemee State Key Laboratory 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 Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB. © 2024 IEEE.

关键词： Clutter (information theory)

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Lightweight NPU Method Based on Hyper-Threading technology

Lightweight NPU Method Based on Hyper-Threading Technology

引用

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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Classification of Bird and Drone Based on Radar time-Frequency Analysis

Classification of Bird and Drone Based on Radar Time-Frequen...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Jialin Li, Weidong Wang, Rui Liu, Haibo Wang, Lianjun Zhang, TianRan Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Ministry of Education Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

To address the classification of 'Low, Small, Slow' targets such as birds and drones, this paper proposes a classification algorithm based on target time-frequency spectrogram analysis. Firstly, radar-collected data is subjected to Short-time Fourier Transform (STFT) to obtain target time-frequency spectrograms, followed by data preprocessing. Subsequently, these time-frequency spectrograms are fed into the designed neural network Residual Pyramid pooling Net (RPNet) for the classification of the two target classes. Validation results based on radar experimental data demonstrate that this algorithm can effectively classify birds and drones, thereby improving the classification performance of small targets. © The Institution of Engineering & technology 2023.

关键词： Drones

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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 ...

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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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