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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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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chen, Gong Liu, Jun Li, Chen-Qian Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350337020

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images. © 2023 IEEE.

关键词： class imbalance multi-task learning pulmonary nodule

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation 9

Multi-Scale and Improved Skip Connections Network for Skin L...

引用

9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Botao Chen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350376548

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's 3× 3 convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation. © 2024 IEEE.

关键词： Problem solving

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Acceleration method for target HRRP generation based on DDPM 2

Acceleration method for target HRRP generation based on DDPM

引用

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

作者： Shen, Xitai Zhou, Qiang Yu, Bingqian Zhang, Xin Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute 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

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity. © 2024 IEEE.

关键词： Radar target recognition

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ren, Mengxi Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important;(2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary;(3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images. © 2024 IEEE.

关键词： Ultrasonic imaging

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects 2

Transductive Multi-Prototype Network for HRRP Recognition wi...

引用

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

作者： Yuan, Mingchen Liu, Jiaqi Wang, Xinyang Li, Yang Wang, Yanhua Zhang, Liang 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

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes. © 2024 IEEE.

关键词： Automatic target recognition

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Multi-Terrain Radar HRRP Target Detection Using Contrastive Learning 2

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

引用

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

作者： Wang, Xinyang Yuan, Mingchen Liu, Jiaqi Li, Yang Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute 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

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target's energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods. © 2024 IEEE.

关键词： Clutter (information theory)

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Missing Modality Completion for Multi Frequency Radar HRRP Recognition Using GAN 2

Missing Modality Completion for Multi Frequency Radar HRRP R...

引用

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

作者： Zhou, Qiang Yu, Bingqian Meng, Yuanhao Zhang, Xin Li, Yang Wang, Yanhua Zhang, Liang 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 one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss. © 2024 IEEE.

关键词： Frequency modulation

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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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An Efficient Transposition Method for Spaceborne SAR Data Based on HBM

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

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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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