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

作者： Yichen Liu Ping-An Liu Liang Zhang Yanhua Wang Zengdi Bao Yinhui Xu Radar Technology Research Institute Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High-resolution range profiles (HRRP) have become increasingly important in radar automatic target recognition (RATR) due to their ability to capture detailed structural features of targets. In practical RATR scenarios, the operational environment is open and dynamic, often exposing models to targets belonging to previously unseen categories. This underscores the necessity for open-set recognition (OSR) methods that can effectively distinguish both known and unknown targets. Theoretically, there exists a distributional discrepancy between the feature representations of known and unknown categories in deep learning models. By designing a suitable confidence calculation method that leverages this discrepancy, it becomes possible to accurately identify whether a given sample belongs to a known or an unknown category. Inspired by the Neural Collapse theory, we observe that deep features representing known categories tend to cluster tightly around the weight vectors of the final layer, whereas features from unknown categories are typically more scattered and lie farther away. Building on this observation, we propose a confidence measure that is determined by the proximity of a feature to the weight vectors of the final classification layer. Samples with confidence values below a defined threshold are classified as unknown, which forms the fundamental mechanism of our open-set detector. Our approach is versatile, making it applicable to a variety of network architectures and adaptable to different feature distance metrics. Extensive experiments conducted on HRRP data confirm the efficacy of our proposed method for open-set recognition, demonstrating its ability to reliably distinguish between known and unknown targets under diverse conditions.

关键词： Measurement Deep learning Weight measurement Training Target recognition Radar Network architecture Vectors Radar signal processing Reliability

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

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

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

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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Adaptation models Sensitivity Target recognition Radar Data collection Feature extraction Generative adversarial networks Data augmentation Data models Data mining

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Lightweight CNN for Radar HRRP Recognition Using NAS-Based Pruning and Multi-Knowledge Distillation

Lightweight CNN for Radar HRRP Recognition Using NAS-Based P...

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

作者： Zhilong Zhang Heke Wu Yinhui Xu Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep convolutional neural network (CNN) has been widely studied in radar target high resolution range profile (HRRP) recognition. However, the CNN with deep structure requires high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we propose a differentiable neural architecture search (NAS)-based channel pruning and multi-knowledge distillation (MKD) to prune and fine-tune the CNN for lightweight. In the pruning stage, NAS method is used to automatically find the good-performance pruning structure by optimizing channel preserve scores. After the search process, these scores are used for global pruning to derive the pruned model. In the fine-tuning stage, MKD method combine the physical scattering knowledge with the category knowledge from pretrained model to restore the pruned model performance. Experimental results on VGG-16 with HRRP dataset inversed from the MSTAR show that the recognition accuracy of the proposed method decreases 1.52% by using 5% model parameters.

关键词： Accuracy Target recognition Neural networks Scattering Radar Data processing Data models Neural architecture search Convolutional neural networks

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Open Set HRRP Recognition Using Adaptive Learning with Enclosed Decision Boundaries

Open Set HRRP Recognition Using Adaptive Learning with Enclo...

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

作者： Binhua Yu Ping-An Liu Yichen Liu Hongyu Wang Liang Zhang Yanhua Wang Kun Liu Radar Technology Research Institute Beijing Institute of Technology Beijing China Xi’an Modern Control Technology Research Institute Xi’an China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

High resolution range profiles (HRRP), which contain fine geometric structure information of targets, plays animportant role in the field of Radar automatic target recognition (RATR). In the actual application of RATR, radar may often capture targets that are non-cooperative, making it difficult to establish a comprehensive database, and traditional recognition algorithms are prone to misjudgment when facing unknown targets. Therefore, Open set recognition (OSR) is proposed to simultaneously recognize in-database and out-of-database targets. Considering the pose sensitivity of HRRP, minor changes in the target’s pose can cause significant changes in the HRRP images, leading to a potentially dispersed distribution of samples from the same category in the feature space, which makes it difficult for traditional fixed decision boundaries in OSR to adapt. To address this, this paper proposes an HRRP open set recognition method capable of adaptively learning closed decision boundaries. Based on Hierarchical dirichlet processes (HDP), the method achieves adaptive clustering of subclasses under different categories according to the data distribution characteristics of each category, determining multiple small decision boundaries within each category, and allowing the decision boundaries to dynamically adjust according to the sample features. Finally, experiments with real HRRP data have verified the effectiveness of the proposed method.

关键词： Training Sensitivity Image resolution Image recognition Target recognition Databases Imaging Radar applications Signal resolution Dispersion

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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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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Drugs Design methodology Vectors Protein sequence Encoding Bioinformatics

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

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

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

作者： Hanpu Zhou Kai Yan Jian Wang Xiaoxiao Li Weiming Tian Yunkai Deng Radar Technology Research Institute Bejing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Building Research Institute Corporation Limited of CSCEC Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Point cloud compression Three-dimensional displays Two-dimensional displays Superresolution Tomography Apertures Radar imaging Taylor series Radar polarimetry Synthetic aperture radar

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Coherence Analysis and Interferometric Measurement of P-Band Repeat Pass UAV InSAR

Coherence Analysis and Interferometric Measurement of P-Band...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xin Xie Yunkai Deng Zihang Jiang Xiaoxiao Li Weiming Tian Radar Technology Research Institute 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 China Beijing Building Research Institute Co. Ltd. of CSCEC Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Miniaturized and lightweight unmanned aerial vehicles (UAV) provide a flexible platform for synthetic aperture radar (SAR). The application of UAV interferometric SAR (InSAR) is gradually increasing in interferometric measurement fields. Leveraging the P-band signal and the capability to penetrate leaf clusters, these UAV InSAR systems demonstrate significant potential for topographic mapping and deformation monitoring, even in challenging surface conditions. This paper introduces the primary challenges associated with UAV InSAR, with a specific focus on analyzing the coherence of UAV InSAR. Additionally, we present interferometric measurement results obtained by our self-developed P-band UAV InSAR system and the processing flow we proposed. These results effectively address key challenges and validate the feasibility of the system for repeat pass interferometric measurements.

关键词： Optical interferometry Deformation Geoscience and remote sensing Coherence Gain measurement Autonomous aerial vehicles Surface topography

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Model Switching Detection-Aided Adaptive Transition Probability Matrix IMM Algorithm

Model Switching Detection-Aided Adaptive Transition Probabil...

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

作者： Youwang Chen Yunkai Deng Weiming Tian Radar Technology Research Institute Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing Advanced Technology Research Institute Beijing Institute of Technology Jinan 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 School of Information and Electronic Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Adaptation models Accuracy Heuristic algorithms Delay effects Switches Numerical simulation Data processing Numerical models

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A Self-supervised SAR Image Despeckling Strategy Based on Parameter-sharing Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Liang Yin, Yifei Shi, Hao Sheng, Qingqing Li, Wei The Radar Research Laboratory Beijing Institute of Technology Beijing10081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401135 China The Beijing Key Laboratory of Embedded Real-time Processing Technology Beijing100081 China The Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing10081 China The School of Information and Electronics Beijing Institute of Technology Beijing10081 China

Speckle noise is generated due to the SAR imaging mechanism, which brings difficulties in SAR image interpretation. Hence, despeckling is a helpful step in SAR pre-processing. Nowadays, deep learning has been proved to be a progressive method for SAR image despeckling. Most deep learning methods for despeckling are based on supervised learning, which needs original SAR images and speckle-free SAR images to train the network. However, the speckle-free SAR images are generally not available. So, this issue was tackled by adding multiplicative noise to optical images synthetically for simulating speckled image. Therefore, there are following challenges in SAR image despeckling: (1) lack of speckle-free SAR image;(2) difficulty in keeping details such as edges and textures in heterogeneous areas. To address these issues, we propose a self-supervised SAR despeckling strategy that can be trained without speckle-free images. Firstly, the feasibility of SAR image despeckling without speckle-free images is proved theoretically. Then, the sub-sampler based on the adjacent-syntropy criteria is proposed. The training image pairs are generated by the sub-sampler from real-word SAR image to estimate the noise distribution. Furthermore, to make full use of training pairs, the parameter sharing convolutional neural networks are adopted. Finally, according to the characteristics of SAR images, a multi-feature loss function is proposed. The proposed loss function is composed of despeckling term, regular term and perception term, to constrain the gap between the generated paired images. The ability of edge and texture feature preserving is improved simultaneously. Finally, qualitative and quantitative experiments are validated on real-world SAR images, showing better performances than several advanced SAR image despeckling methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

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Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain

arXiv

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arXiv 2022年

作者： Zhang, Tong Gao, Peng Dong, Hao Zhuang, Yin Wang, Guanqun Zhang, Wei Chen, He Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shang Hai AI laboratory Shanghai100024 China Peking University Beijing100871 China

Currently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. It has reached the status of consensus solution for task-aware model training in remote sensing domain (RSD). Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pretraining in RSD. Moreover, pretraining models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable labeling noise, severe domain gaps and task-aware discrepancies. Thus, in this paper, considering the self-supervised pretraining and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive PreTraining (CSPT) is proposed based on the idea of not stopping pretraining in natural language processing (NLP), which can gradually bridge the domain gap and transfer knowledge from the nature scene domain to the RSD. The proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Finally, extensive experiments are carried out on twelve datasets in RSD involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and SAR). The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in RSD can outperform the previous method of supervised pretraining-then-fine-tuning and even surpass the state-of-the-art (SOTA) performance without any expensive labeling consumption and careful model design. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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