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

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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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An Improved Method for Rockfall Detection and Tracking Based on Video Stream

An Improved Method for Rockfall Detection and Tracking Based...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Longyue Wang, Songge Xie, Xin Deng, Yunkai Tian, Weiming Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (纸本)9781839539954

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. © The Institution of Engineering & technology 2023.

关键词： Rock bursts

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An Extremely Pipelined FPGA-based accelerator of All Adder Neural Networks for On-board Remote Sensing Scene Classification

An Extremely Pipelined FPGA-based accelerator of All Adder N...

引用

IEEE International Conference on Field-Programmable technology (FPT)

作者： Ning Zhang Shuo Ni Tingting Qiao Wenchao Liu He Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

Directly completing remote sensing scene classification (RSSC) on space platforms can minimize latency and relieve data downlink burdens. The all adder neural network $( \mathrm{A}^{2}$NN) is a novel network for on-board RSSC with lower resource overhead than convolutional neural networks (CNNs). However, most of the existing FPGA-based accelerators are designed for CNNs and are not applicable to deploy $\mathrm{A}^{2}$NNs. In this paper, we propose an extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NNs and implement a VGGNet-11 backbone for on-board RSSC. In the proposed FPGA-based accelerator, an extremely pipelined processing engine (PE) suitable for accelerating the adder layer is designed. Each adder layer in the $\mathrm{A}^{2}$NN is mapped to a dedicated extremely pipelined PE, which achieves low-latency calculations. Besides, the entire parameters of the network are stored in block RAMs, and the intermediate data are cached on the FPGA chip, thereby eliminating external memory accesses and reducing power consumption. To evaluate the performance of the proposed extremely pipelined FPGA-based accelerator of $\mathrm{A}^{2}$NN, we implemented an $\mathrm{A}^{2}$NN-based RSCC model with the VGGNet-11 backbone on the Xilinx Virtex7 XC7VX690T FPGA by the proposed accelerator. The experimental results show that the proposed FPGA-based accelerator of $\mathrm{A}^{2}$NNs can achieve a throughput of 3.04 tera operations per second (TOPs) at 200 MHz while consuming 8.27 W.

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Multi-Grained Global-Local Semantic Feature Fusion for Few Shot Remote Sensing Scene Classification

Multi-Grained Global-Local Semantic Feature Fusion for Few S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yuqing Liu Tong Zhang Yin Zhuang Guanqun Wang He Chen Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

Few-shot remote sensing scene classification aims to classify unseen scenes by using only a few labeled samples. Hence, how to set up a more effective feature description according to a few labeled samples, becomes an important issue. In this paper, in view of more complicated remote sensing scenes containing several hierarchical and coupled spatial relations (e.g., internal and external spatial contexts), which severely hinder the feature extraction under few-shot learning scenarios, a multi-grained global-local semantic feature fusion (MGGL-SFF) method is proposed for few-shot remote sensing scene classification, which can better combine the global discriminative spatial semantic features with local transferable fragment features to set a powerful prototype representation up for few shot learning. Finally, experiments are carried out on defined few-shot remote sensing scene classification benchmark, and results proved the proposed MGGL-SFF can achieve a new state-of-the-art performance.

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An FPGA-GPU Heterogeneous System and Implementation for On-Board Remote Sensing Data processing

An FPGA-GPU Heterogeneous System and Implementation for On-B...

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IEEE International Conference on Field-Programmable technology (FPT)

作者： Tingting Qiao Yu Xie He Chen Yizhuang Xie Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

In resource-constrained space environments, energy-efficient and high-performance processors like Field- Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) are widely utilized for on-board data processing. However, the variety of tasks in space missions, including remote sensing data pre-processing and processing, reveals the limitations of single processors. To address this, we suggest a heterogeneous system that combines FPGAs with embedded Graphics processing Unit (GPU) TX2 for efficient remote sensing data pre-processing and processing. Additionally, to overcome the challenges in data transfer between the FPGA and TX2, we utilized High-Level Synthesis (HLS) to design a dedicated IP for efficient data transfer between the FPGA and TX2 using PCIe protocol. This method enhances data transfer speeds and enables TX2 to manage address mapping in the Double Data Rate (DDR) memory. Our system successfully completes remote sensing data processing by integrating these elements.

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A Quantitative Estimation Method of Migrating Insects Using Weather Radar by High Resolution Radar Joint Observation

A Quantitative Estimation Method of Migrating Insects Using ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Jiayi Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and Electronic Beijing Institute of Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

The main migration animals in China are insects, and a large number of large-scale seasonal migration insects have a great impact on ecology. The establishment of a comprehensive early warning system for migratory insects necessitates the implementation of large-scale macro monitoring. Among the array of methodologies available, weather radar stands out as an efficacious tool for facilitating extensive macro-level monitoring. Nonetheless, several unresolved challenges persist in accurately quantifying the density of vast insect populations on a large scale. This article introduces an innovative method for insect quantification through the collaborative use of weather radar and high-resolution phased array radar for insect target observation and joint data analysis. This novel method harnesses the collective observational data derived from both weather radar and high-resolution radar systems, harmonizing the temporal and spatial dimensions of these datasets. By establishing a nuanced mapping correlation between insect density and weather radar reflectivity, this method enables the determination of insect density within specific geographical regions. In the final part of this article, joint observation experiments were conducted to examine the experimental data collected from operating weather radar and specialized phased array radar systems. A total of 244 days of data were analysed, and the proposed method was rigorously validated. © The Institution of Engineering & technology 2023.

关键词： Radar reflection

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Partial Triple Modular Redundancy Method for Fault-Tolerant Circuit based on HITS Algorithm

Partial Triple Modular Redundancy Method for Fault-Tolerant ...

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IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems

作者： Yu Xie Wen-Yue Yu Ning Zhang He Chen Yi-Zhuang Xie Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

Circuits related to nuclear facilities and on-orbit circuits typically require fault-tolerant reinforcement to cope with harsh radiation environments. While the traditional fully Triple Modular Redundancy (TMR) method can effectively reinforce circuits, it suffers from the problems of large circuit area and high power consumption. To solve this problem, this paper proposes a Partial TMR method based on the HITS algorithm (Hyperlink-Induced Topic Search), which can effectively perform circuit fault tolerance. This method sorts the importance of triggers in the circuit based on the HITS algorithm and uses the Fault Interval time (MTBF) of the Bi-Logic Cone model as the judgment criterion to determine the number of triggers that need to be redundantly designed under partial TMR. Some commonly used modules of an on-orbit processor were tested to verify the effectiveness of the proposed redundancy method in this paper. The experimental results show that the resource overhead can be reduced by at least 20% while achieving an MTBF of 99.9% under TMR.

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A tracking filter algorithm for migratory birds based on group situation constraints

A tracking filter algorithm for migratory birds based on gro...

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

作者： Biao Li Rui Wang Jiong Cai The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm.

关键词： Target tracking Heuristic algorithms Dynamics Radar Filtering algorithms Radar tracking Prediction algorithms Birds information filters Radar signal processing

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Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder 2

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

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

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China 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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Wavelet decomposition

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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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