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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...

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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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Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

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

引用

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

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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Sparse View HRRP Recognition Based on Dual-Task of Generation and Recognition Method 2

Sparse View HRRP Recognition Based on Dual-Task of Generatio...

引用

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

作者： Meng, Yuanhao Wang, Lue Zhou, Qiang Zhang, Xin Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Modern Control Technology Research institute Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

In recent years, high-resolution range profiles (HRRPs) have played a significant role in radar target recognition. The pose sensitivity of HRRPs results in unsatisfactory target recognition capabilities under sparse viewing angles. However, for non-cooperative targets, obtaining full-view HRRPs is often challenging, leading to suboptimal performance in HRRP-based target recognition. To address the issue of target recognition under sparse viewing angles, we propose an end-to-end model DGR for sparse view recognition that integrates combined generation and recognition tasks. The DGR model extracts deep features of HRRPs through an attention mechanism and utilizes these features for both target recognition and the generation of adjacent-view HRRPs, with both tasks mutually reinforcing each other for improved performance in recognition and data generation. Furthermore, by employing domain adaptation methods, the model learns the variability of target HRRPs in the source domain and applies this knowledge to the target domain, enhancing its generalization ability. Experimental results demonstrate that the DGR model achieves optimal recognition and generation capabilities simultaneously. © 2024 IEEE.

关键词： radar target recognition

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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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Classification of Bird and Drone Based on radar time-Frequency Analysis

Classification of Bird and Drone Based on Radar Time-Frequen...

引用

IET International radar Conference 2023, IRC 2023

作者： Li, Jialin Li, Weidong Wang, Rui Liu, Haibo Wang, Lianjun Zhang, TianRan 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 Embedded Real-time Information Processing Technology Beijing Institute of Technology Ministry of Education Beijing100081 China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9781839539954

To address the classification of 'Low, Small, Slow' targets such as birds and drones, this paper proposes a classification algorithm based on target time-frequency spectrogram analysis. Firstly, radar-collected data is subjected to Short-time Fourier Transform (STFT) to obtain target time-frequency spectrograms, followed by data preprocessing. Subsequently, these time-frequency spectrograms are fed into the designed neural network Residual Pyramid pooling Net (RPNet) for the classification of the two target classes. Validation results based on radar experimental data demonstrate that this algorithm can effectively classify birds and drones, thereby improving the classification performance of small targets. © The Institution of Engineering & technology 2023.

关键词： Drones

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Lightweight NPU Method Based on Hyper-Threading technology

Lightweight NPU Method Based on Hyper-Threading Technology

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Deep learning Computer architecture Logic gates Data processing Hardware Table lookup Field programmable gate arrays Signal resolution Logic arrays Testing

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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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radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

引用

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

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China 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 Jinan250300 China

ISBN: (纸本)9798331515669

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB. © 2024 IEEE.

关键词： Clutter (information theory)

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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...

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

Radar Echo Modeling and Doppler Characteristic Fitting Metho...

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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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