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A Dynamic Short-range Animal Migration Forecast Model Based on Weather Radar Network

A Dynamic Short-range Animal Migration Forecast Model Based ...

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IET International Radar Conference 2023, IRC 2023

作者： Liu, Xuan Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Wu, Dongli Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Meteorological Observation Centre of China Meteorological Administration Beijing100081 China

ISBN: (纸本)9781839539954

Predicting aerial animal migration has great implications for biological research and conservation, and agricultural production. Animal migration is deeply coupled with spatio-temporal and meteorological factors, however, the existing forecast model is only based on the spatio-temporal features of animal migration and the impact of meteorological factors on migration is not considered. In this paper, we propose a dynamic forecast model to forecast airborne migration from the perspective of the influence of wind field data on migration and makes prediction with the help of neural network. Specifically, we calculate the transition probability matrix using the wind data and the migration spatial field to represent the migration dynamic model, and integrated this model into deep neural network. We use the data of China's weather radar network to verify that our model outperforms the competing methods. In addition, the cause of prediction error at peak value is analysed by visualization results. In future applications, more meteorological factors should be considered to improve the representation of dynamic models. © The Institution of Engineering & technology 2023.

关键词： Wind forecasting

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A Biological Echo Identification Method with Single Polarized Weather Radar Using Super-Pixel Segmentation

A Biological Echo Identification Method with Single Polarize...

引用

IET International Radar Conference 2023, IRC 2023

作者： Yan, Zujing Cui, Kai Zhang, Jingmin Wang, Rui Hu, Cheng Liu, Jiaxing Zhang, Duo Sun, Zhuoran Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China No. 208 Research Institute of China Ordnance Industries Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing 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

ISBN: (纸本)9781839539954

Weather radar networks, given their advantages of continental coverage and year-round operation, show great potential in the study of large-scale aerial biological migration. The precise identification and extraction of biological echoes from vast amounts of weather data serve as the essential metrics for assessing the biological monitoring capability of weather radar systems. However, complex weather conditions pose an obstacle to extracting biological information accurately. This study introduces super-pixel segmentation approach in the domain of weather radar-based biological recognition. A cluster-based method is proposed to extract biological information from localized spatial weather radar rendered images. From the perspective of colour space, we confirm the necessity and evident benefits of differentiating them at local spatial level. We collected 750,000 historical records from 108 weather radars nationwide and handpicked 36,000 representative migration cases for labelling, creating a large-scale trainable dataset for studying aerial ecological migration patterns with weather radars. We apply the proposed method to two typical scenes, and the results demonstrate that the accuracy of the proposed method is above 89.38 % in the whole scenes. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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

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

引用

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Image quality Image resolution Object detection Interference information filters Data processing Image enhancement

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

Radar Echo Modeling and Doppler Characteristic Fitting Metho...

引用

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Analytical models Radar cross-sections Target recognition Fitting Gaussian distribution Autonomous aerial vehicles Data models Doppler radar Doppler effect Data mining

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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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ANALYSIS ON SPATIAL RESOLUTIONS OF LOW-FREQUENCY ULTRAWIDEBAND WIDE BEAMWIDTH SAR

ANALYSIS ON SPATIAL RESOLUTIONS OF LOW-FREQUENCY ULTRAWIDEBA...

引用

IET International Radar Conference 2023, IRC 2023

作者： Xie, Xin Wang, Jian Deng, Yunkai Yang, Zhijun Tian, Weiming Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Building Research Institute Co. Ltd. of CSCEC Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

Ultrawideband Synthetic Aperture Radar (UWB-SAR) is a high-resolution imaging radar with leaf-cluster penetration capability, which has broad application prospects in military and civilian fields. It can accurately map the terrain covered by the forest and imaging various targets hidden in forests. Compared with the conventional high-frequency narrow-band narrow-beamwidth (NBNB) SAR, the low-frequency UWB-SAR has the characteristics of large relative bandwidth and large imaging integral angle, and its imaging results will inevitably have new characteristics. Aiming at this phenomenon, this paper analyses the imaging characteristics of low-frequency ultrawideband wide beam SAR by point spread function. Firstly, the point spread function of low-frequency UWB-SAR is derived and its characteristics are analyzed. Then, the side-lobe direction and two-dimensional spatial resolution characteristics of the imaging results are analyzed according to the point spread function, and the relationship between spatial resolution, relative bandwidth and beamwidth is given by simulation. © The Institution of Engineering & technology 2023.

关键词： Optical transfer function

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Tiny Target Detection with Adaptive Weighted Feature Fusion in UAV Images

Tiny Target Detection with Adaptive Weighted Feature Fusion ...

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

作者： Xiaohui Kang Lingjie Zheng Zhiyuan Deng Yuqi Han Linbo Tang Chenwei Deng 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 Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Accuracy Convolution Semantics Object detection Transforms Feature extraction Autonomous aerial vehicles Remote sensing Drones Lenses

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