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检索条件"机构=Key Laboratory of Embedded Real-time Information Processing Technology"

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DATA-DRIVEN SAR IMAGE RECONSTRUCTION METHOD USING ADMM WITH DEEP PLUG-AND-PLAY PRIORS

DATA-DRIVEN SAR IMAGE RECONSTRUCTION METHOD USING ADMM WITH ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Ziwen Wei, Yangkai Ding, Zegang Shan, Xueting Wu, Yifan School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Synthetic Aperture Radar (SAR) image reconstruction fundamentally constitutes an ill-posed reconstruction conundrum, with the overarching objective of faithfully restoring the'authentic' SAR scenes via radar echo measurements and a forward model. The conventional FFT-based approach, in conjunction with matched filtering, tends to yield undesirable sidelobes and speckle artifacts. Conversely, the reconstruction challenge can be cast as a minimization problem, seeking to enhance outcomes by merging data-term and prior-related terms. This composite problem is amenable to resolution via proximal methods. In this paper, a novel data-driven methodology is employed, leveraging the plug-and-play (PNP) technique to train a neural network for acquiring target image priors. In comparison to traditional manually crafted priors and PNP priors, the proposed PNP deep priors exhibit the capacity to achieve superior reconstruction results. Experimental assessments involving synthetic and real SAR scenes underscore the robustness and efficacy of the proposed approach. © The Institution of Engineering & technology 2023.

关键词： Matched filters

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DEMONSTRATION OF P BAND UAV BISTATIC SAR EXPERIMENT AND IMAGING processing

DEMONSTRATION OF P BAND UAV BISTATIC SAR EXPERIMENT AND IMAG...

引用

IET International Radar Conference 2023, IRC 2023

作者： Ma, Xinnong Li, Linghao Ding, Zegang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

UAV Bistatic SAR (UAV BiSAR) distributes the transmitting and receiving system on different UAVs, which has the advantages of anti-reconnaissance, powerful crypticity and anti-interference capability. However, due to problems such as channel delay, radio frequency interference(RFI), and synchronization error, it is difficult to perform 2D imaging on the UAV BiSAR real data. Therefore, the results of UAV BiSAR real data are rare in the existing literature. The P-band UAV BiSAR real data based on two-way convection performed in this paper can well solve the above problems. Next, we will demonstrate the P-band UAV BiSAR two-dimensional imaging experiment. Finally, some imaging results are given to verify the P-band UAV BiSAR mode and processing method. © The Institution of Engineering & technology 2023.

关键词： Radio interference

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Modeling and Verification of processing Engines in SAR Imaging SoC 2

Modeling and Verification of Processing Engines in SAR Imagi...

引用

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

作者： Zhang, Zhihan Yang, Chao Xie, Yizhuang Li, Yongrui Zhang, Ao Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Lab School of Information and Electoronics Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798331515669

With the development of real-time requirements for Synthetic Aperture Radar (SAR) imaging technology, SAR imaging processing SoC (System on Chip) has become an important research field at present. The subsystem for SAR imaging processing in the SAR imaging SoC needs to be fully verified during the development process. This article designs a UVM-based random verification environment, focusing on the verification requirements of key computing engines and interaction engines in the SAR imaging processing SoC. At the same time, a modeling method for computing engines and interaction engines is proposed, which can quickly and accurately describe the functions and behaviors of processing engines. Collaborating with the verification environment and the reference C model, the design under test achieves satisfactory coverage results. Consequently, the functionality of the processing engines within the subsystem can be comprehensively and effectively verified. The content studied in this article has certain value for the engineering verification implementation of SAR imaging algorithms in the SoC. © 2024 IEEE.

关键词： System-on-chip

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GROUND-BASED DISTRIBUTED RADAR SYNCHRONIZATION WITH LONG COHERENCE time

GROUND-BASED DISTRIBUTED RADAR SYNCHRONIZATION WITH LONG COH...

引用

IET International Radar Conference 2023, IRC 2023

作者： Yan, Junjie Li, Linghao Ding, Zegang Ma, Xinnong Wei, Yangkai Gao, Yibo School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Ground-based distributed radar has received increasing attention and research from scholars due to its strong aerospace targets detection capability. However, due to its unique distributed configuration, the differences in frequency sources among stations can cause great synchronization trouble for imaging. In low signal-to-noise ratio (SNR) echo environment and with long coherence time, the SNR gain provided by pulse compression is insufficient to extract the range history of the significant point. This makes traditional echo-based synchronization methods no longer applicable. In this paper, a novel synchronization method based on significant point under low SNR is proposed, and simulation has verified its effectiveness. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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real-time Object Detection in UAV Vision based on Neural processing Units 6

Real-Time Object Detection in UAV Vision based on Neural Pro...

引用

6th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Liu, Ming Tang, Linbo Li, Zongya Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781665431859

With the reduction of Unmanned Aerial Vehicle (UAV) hardware cost and the development of deep learning algorithm, the real-time object detection algorithm applied in UAV vision has great advantages in many fields. However, due to the limited energy consumption and computing power of embedded devices used in the drones and the variable object scales and complex backgrounds in the UAV vision restrict the applications in object detection based on the drones. In this paper, we optimized the generation of anchor boxes, introduced a new module to increase the receptive field to improve the detection of small targets, and used adaptively spatial feature fusion in the feature pyramid to increase feature fusion of multi-scale features. At last we pruned the model to make it lighter and faster, and got the Average Precision (AP) of 89.7% for UAV car aerial images and the speed of 35.7 FPS by running on Neural processing Units (NPUs), which proves the feasibility of the intelligent object detection algorithm's efficient processing in hardware resource limited environment. © 2022 IEEE.

关键词： Energy utilization

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Radar Echo Modeling and Doppler Characteristic Fitting Method for UAVs Based on Multi-Scattering Point Model 2

Radar Echo Modeling and Doppler Characteristic Fitting Metho...

引用

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

作者： Xu, Li'Ang Jiang, Qi Wang, Rui Zhang, Jichuan Jiao, Longxiang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Technology Advanced Technology Research Institute Beijing China

ISBN: (纸本)9798331515669

The radar echo characteristics of unmanned aerial vehicles provide important information for identification. The radar echoes of various types of UAVs are modeled by the multi-scattering point modeling method. A Doppler characteristic modeling method for unmanned aerial vehicles based on the mixture Gaussian algorithm is proposed, in which the slow-time dimension data are extracted from the Range- Doppler and fitted by the mixture Gaussian algorithm. The parameters of the Gaussian distribution components representing the unmanned aerial vehicle's Doppler spread characteristics are obtained using the algorithm. The simulated radar echoes of the multi-rotor unmanned aerial vehicle and the flapping-wing unmanned aerial vehicle are used for analysis. The performance of the fitting is acceptable and the way in which various parameters affect the Doppler characteristics is discussed and concluded. © 2024 IEEE.

关键词： Gaussian distribution

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Lightweight NPU Method Based on Hyper-Threading technology 2

Lightweight NPU Method Based on Hyper-Threading Technology

引用

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

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Integrated circuit design

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A method for enhancing infrared small target images under complex sky backgrounds 2

A method for enhancing infrared small target images under co...

引用

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

作者： Xu, Yuhan Tang, Linbo Sun, Haotian Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Radar Research Lab School of Information and Electronics Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

Infrared target detection is currently widely used in both military and civilian fields. Due to the lower resolution of infrared images compared to visible light images and the lack of texture information in small targets, the quality of infrared small target images is often poor and not suitable for direct detection. Therefore, enhancing low-quality infrared small target images is a necessary step before object detection can be performed. This article presents a method for enhancing infrared images of small targets. The process begins with applying guided filtering to the original image. The resulting detail layer is then used as a guided image for a second guided filtering of the base layer, producing a second detail layer. Additionally, a novel approach to local contrast enhancement is introduced, which applies contrast enhancement specifically to this secondary detail layer. Experimental results has been demonstrated that the enhancement method proposed in this paper significantly improves the visibility of the target area while reducing background interference, without losing information about small targets. Additionally, this method outperforms other common techniques in terms of enhancement effect, as shown in comparative experiments. © 2024 IEEE.

关键词： Wiener filtering

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Tiny Target Detection with Adaptive Weighted Feature Fusion in UAV Images 2

Tiny Target Detection with Adaptive Weighted Feature Fusion ...

引用

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

作者： Kang, Xiaohui Zheng, Lingjie Deng, Zhiyuan Han, Yuqi Tang, Linbo Deng, Chenwei Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology Advanced Technology Research Institute Jinan250300 China Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

With the development of drones, imaging lenses and other equipment, effective and accurate detection of tiny targets in drone remote sensing images has become a hotspot. However, the features of tiny targets are weak and difficult to extract. Most existing methods enhance the target feature expression by directly superimposing and fusing multi-scale feature maps. Only the features with stronger responses in adjacent feature maps can be enhanced, while the targets with weaker responses may be diluted. For this reason, an adaptive weighted fusion small target detection algorithm is designed. To enhance the proportion of small targets in feature maps of different resolutions, a bidirectional feature pyramid network is designed. The adaptive weights are set according to the feature density in the feature map, and they will be used to perform bidirectional fusion on feature maps to reduce the interference of background and large targets on the features of small targets. Besides, we design multi-scale dilated convolution, which is injected into the feature fusion network. In this way, we successfully enhance the expression of small target feature. Finally, we test the algorithm via the public dataset VisDrone, the results show that our methods significantly improve the accuracy of small target detection. © 2024 IEEE.

关键词： Drones

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Fine grained image recognition algorithm based on CNN-Transformer and paired interaction 4

Fine grained image recognition algorithm based on CNN-Transf...

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4th International Conference on Neural Networks, information and Communication Engineering, NNICE 2024

作者： Wang, Yu Zhang, Hong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350394375

A new fine-grained image recognition algorithm (P-ResViT, Paired Interaction Res50-ViT) based on CNN-Transformer is proposed to address the issue of existing fine-grained image recognition methods based on convolutional neural networks (CNN) lacking attention mechanisms and difficulty in accurately locating key regions, as well as weak ability to extract contextual information and global features. This algorithm introduces paired interaction attention mechanisms to supplement external attention. This algorithm can utilize the self attention mechanism of the Vision Transformer and the external attention mechanism formed by paired image inputs during training to achieve precise positioning, while preserving the inductive bias and local feature extraction capabilities of CNN. Firstly, the fine-tuning improved ResNet-50 completes local feature extraction and obtains high-resolution feature maps. Secondly, the Transformer encodes the tokenized image blocks of the feature map as input sequences for extracting global context. Finally, the Transformer completes global feature extraction and outputs the classification sequence. In addition, two external attention mechanisms were introduced by inputting image pairs during training, including collaborative attention mechanisms for similar images and cross attention mechanisms for heterogeneous images, fully utilizing the correlation information between images. After experimental evaluation, the P-ResViT proposed in this article has reached advanced levels in three fine-grained image datasets CUB, CAR, and AIR. © 2024 IEEE.

关键词： Image recognition

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