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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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Remote Sensing Object Detection Based on Lightweight YOLO-V4 7

Remote Sensing Object Detection Based on Lightweight YOLO-V4

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

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

引用

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

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

引用

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

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

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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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