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Research on Ground Clutter Polarization Suppression Algorithm Based on Prior information

Research on Ground Clutter Polarization Suppression Algorith...

引用

IET International Radar Conference 2023, IRC 2023

作者： Liu, Sheng Cai, Jiong Zhang, Jichuan Wang, Rui Hu, Cheng 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

ISBN: (纸本)9781839539954

Low-altitude unmanned aerial vehicle (UAV) targets are challenging to detect by radar due to their low flight altitude, which exposes them to strong interferences from complex ground clutter. To address this, this paper proposes a ground clutter polarization suppression method based on prior information, facilitating the effective detection of low-altitude UAV targets. Initially, the method establishes a clutter prior information database and a clutter map according to the actual radar application environment, classifying different types of clutter. This serves as the prior information for clutter, supporting detection in the context of complex ground clutter backgrounds. Subsequently, a linear polarization filtering algorithm optimized based on maximizing the signal-to-clutter ratio (SCR) is proposed for specific clutter environments. A strong clutter point is selected from the clutter map, and the effectiveness of the algorithm proposed in this paper is analyzed and verified using real-world radar data collected from field measurements. The results indicate that the proposed algorithm improves the average signal-to-clutter ratio by 15.39 dB compared to single polarization channels. This method provides a reference for complex ground clutter polarization suppression and polarization detection. © The Institution of Engineering & technology 2023.

关键词： Unmanned aerial vehicles (UAV)

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Remote Sensing Object Detection Based on Lightweight YOLO-V4 7

Remote Sensing Object Detection Based on Lightweight YOLO-V4

引用

7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Li, Keng Cao, Yunfei Chen, He Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781510656437

The remote sensing object detection algorithms based on deep learning have high detection performances, but the network structures are too complexity to meet the real-time processing requirements in on-board remote sensing object detection. In order to solve this problem, we proposed a lightweight YOLO-v4 network, which is 76% smaller than the original YOLO-v4. As for the decrease of lightweight network's accuracy, we adopted the general instance distillation algorithm, which used the original YOLO-v4 network as the teacher network and whose detection accuracy achieved 2.1% mAP gain. © 2022 SPIE. All rights reserved.

关键词： Remote sensing

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A Novel Insect Wingbeat Frequency Extraction Algorithm Based on Ambiguity Function

A Novel Insect Wingbeat Frequency Extraction Algorithm Based...

引用

IET International Radar Conference 2023, IRC 2023

作者： Ye, Zihan Li, Weidong Wang, Rui Zhang, Fan Wang, Jiangtao Hu, Cheng 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 Real-Time Information Processing Technology of Embedded Beijing100081 China

ISBN: (数字)9781837240982

ISBN: (纸本)9781839539954

Accurate extraction of wingbeat frequency is crucial in insect radar for behavior analysis and species identification of migratory insects. However, the accuracy of extraction cannot be guaranteed due to the weak wingbeat signals and complex insect targets. To address this, this paper proposes an innovative method for enhancing insect wingbeat frequency extraction. This algorithm uses the periodic characteristics of wingbeat micro-motion signals to extract its delay frequency signal to locate its maximum value through the ambiguity function. Then, extract the insect wingbeat frequency by smoothing compensation and Fourier transform, which improves accuracy of insect wingbeat frequency extraction. The efficacy of our algorithm is validated through comprehensive simulations and field experiments, affirming its potential as a robust solution. © The Institution of Engineering & technology 2023.

关键词： Invertebrates

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SAR TOMOGRAPHY IN URBAN AREAS: AN APPROACH BASED ON SPARSE SPECTRUM FITTING

SAR TOMOGRAPHY IN URBAN AREAS: AN APPROACH BASED ON SPARSE S...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhao, Jian Wang, Zhen Ding, Zegang Sun, Tao Chen, Zhizhou Chen, Haolong Zeng, Tao 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 Chongqing401331 China

ISBN: (纸本)9781839539954

Tomography Synthetic Aperture Radar (TomoSAR) technology, as an extension of Interferometric SAR (InSAR), solves the inherent layover problem and provides three-dimensional (3D) imaging ability. Now, it is a promising technique in urban management and critical situation detection. However, in practical applications, the sparse and non-uniform distribution characteristics of the repeat-pass baselines make TomoSAR face the problem of low resolution and high sidelobes, which affect the 3D imaging effect. To address the above problems, a sparse spectrum fitting (SpSF) based TomoSAR processing approach is proposed in this paper. Compared with the traditional spatial spectrum estimation method, this method can achieve higher resolution and lower sidelobes, which is a significant advantage. The proposed method is also validated using computer simulation and the TerraSAR-X real data. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Research on a Method of realizing High Performance Ultra-Long Pulse Compression Processor Based on FPGA

Research on a Method of Realizing High Performance Ultra-Lon...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wu, HongHao Qiao, TingTing Chen, He Xie, YiZhuang Li, YongRui 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 Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China

ISBN: (纸本)9781839539954

Pulse compression plays a vital role in radar signal processing. With the rapid development of synthetic aperture radar (SAR) imaging technology, the demand for spaceborne SAR imaging processing is becoming more and more urgent, especially the need to support the real-time application of ultra-long pulse compression accelerators. However, limited on-chip resources and memory access bandwidth have always been key bottlenecks restricting the design of ultra-long pulse compression processing. FPGA is widely regarded as a processor that is both highly parallelized and has excellent computing speed. Therefore, this research is committed to proposing a new processor design method based on FPGA to cope with ultra-long point pulse compression processing. First, to be able to effectively handle ultra-long sequence pulse compression up to 1M points, we propose an architecture with careful optimization. Secondly, in order to improve processing efficiency and reduce resource overhead, we propose a fixed-point data fitting implementation strategy. Finally, this study also explores an on-chip storage optimization method for reference function spectrum for linear frequency modulation (LFM) signals, which not only effectively reduces the cost of on-chip resources, but also saves storage transmission bandwidth. These innovative designs and optimization strategies will provide strong support for further development in the field of ultra-long-point pulse compression processing. © The Institution of Engineering & technology 2023.

关键词： Bandwidth compression

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SAR-DARTS: A CONVOLUTIONAL NEURAL ARCHITECTURE SEARCH FOR SAR SCENE CLASSIFICATION

SAR-DARTS: A CONVOLUTIONAL NEURAL ARCHITECTURE SEARCH FOR SA...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Tian Xu, Yinsheng Li, Qihai Qi, Baogui Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839539954

In recent years, the collaborative service architecture of cloud computing and edge computing to reduce the demand for computing, storage and other capabilities of artificial intelligence methods has developed rapidly, but their application in synthetic aperture radar (SAR) image scene classification is still immature. In this paper, we propose a neural architecture search and pruning model for SAR scene classification. By introducing network morphisms and path regularization method, we can reduce the unfair competition between cells on the basis of expanding the search space. The model approach is applied to the FUSAR-Ship and OpenSARShip datasets for performance evaluation. The proposed method can achieve higher accuracy with fewer parameters. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haoran Lin, Xiaoli Hu, Jing Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350386226

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work. © 2024 IEEE.

关键词： Molecular docking

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A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATION FOR SAR SCENE CLASSIFICATION

A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATI...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Qihai Zhang, Jiawei Zhou, Tian Qi, Baogui Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839539954

In recent years, with the advancement of deep learning, convolutional neural networks tailored for processing image content have gained increasing attention within the remote sensing imagery domain. However, deep convolutional neural networks that exhibit exceptional performance often encompass a substantial number of parameters, thereby necessitating heightened computational resources and time for network training and prediction. Given the constraints posed by resource limitations in the realm of remote sensing, deploying such high-performance networks onto these devices becomes a challenging endeavor. To address this set of challenges, this paper introduces a methodology grounded in target differential probability for multi-teacher knowledge distillation. This approach effectively reduces the model's complexity while preserving the network models' commendable performance. By conducting a statistical analysis of the model's output probability distribution and computing the differential probabilities between teacher and student networks, we regulate the output proportions of teachers within the multi-teacher model. As a result, a high-performing student network is cultivated. In aggregate, experimental validation on two distinct datasets substantiates the efficacy of this approach in facilitating efficient knowledge transfer, consequently yielding heightened performance outcomes. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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An Efficient Transposition Method for Spaceborne SAR Data Based on HBM

An Efficient Transposition Method for Spaceborne SAR Data Ba...

引用

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

作者： Yongrui Li HuShan Lv Ming Xu Liang Chen Zhu Yang Yizhuang Xie Radar Technology Research Institute Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The onboard real-time processing of Spaceborne synthetic aperture radar (SAR) is of great significance for the rapid acquisition of surface information. As a key step of SAR imaging processing, the efficiency of data transposition directly affects the imaging quality and processing speed. In order to solve the problem of traditional double data rate synchronous dynamic random-access memory (DDR SDRAM) limiting the transposition speed, this paper proposes a SAR data transposition method based on high bandwidth memory (HBM). The method makes full use of the high bandwidth and multichannel characteristics of HBM, and significantly improves the transposition speed by optimising the data read/write process, providing a new solution for real-time imaging processing of satellite-borne SAR.

关键词： Limiting Spaceborne radar Imaging Bandwidth Radar imaging real-time systems Radar polarimetry SDRAM Synthetic aperture radar Surface treatment

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism 5

Lightweight Monocular 3D Object Detection Based on Attention...

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5th Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2024

作者： Zhan, Feng Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan30065 China

ISBN: (纸本)9798350374407

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed. © 2024 IEEE.

关键词： Object recognition

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