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IET International Radar Conference (IRC 2023)

作者： Ming Xu Jiawei Zhang Yongrui Li Yifei Yin Ao Zhang Heng Dong Liang Chen Hao Shi Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 201109 People's Republic of China

ISBN: (数字)9781837240982

Spaceborne Synthetic Aperture Radar (SAR) plays an important role in the field of emergency disaster reduction. In order to meet the stringent timeliness requirements in onboard SAR data processing, this paper proposes a configurable four-channel parallel FFT processor, which serves as the core of SAR imaging processing. This processor is capable of supporting up to 128K-point FFT operations. Additionally, it supports radix-2 time decimation FFT concatenation processing, radix-4 time decimation FFT concatenation processing, radix-2 frequency decimation FFT concatenation processing, and radix-4 frequency decimation FFT concatenation processing, all of which can be flexibly configured based on data structure . This processor is implemented on the Xilinx Virtex-7 XC7VX690T FPGA and can achieve a maximum FFT processing capability of 128K points at a frequency of 300MHz. The evaluation and analysis results demonstrate that the processor reaches a higher processing rate, effectively meeting the demanding requirements for efficient spaceborne processing.

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SAR-DARTS: a convolutional neural architecture search for SAR scene classification

SAR-DARTS: a convolutional neural architecture search for SA...

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IET International Radar Conference (IRC 2023)

作者： Tian Zhou Yinsheng Xu Qihai Li Baogui Qi He Chen Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 200240 People's Republic of China

ISBN: (数字)9781837240982

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.

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Open Set Radar HRRP Recognition Using Confidence through Neural Weight Proximity

Open Set Radar HRRP Recognition Using Confidence through Neu...

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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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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 (IRC 2023)

作者： Qihai Li Jiawei Zhang Tian Zhou Baogui Qi He Chen Liang Chen Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing 100081 People's Republic of China Shanghai Institute of Satellite Engineering Shanghai 200240 People's Republic of China

ISBN: (数字)9781837240982

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 pro portions 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.

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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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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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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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An Anti-background Interference Object Detection Method for Complex Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Tang, Wei Zhao, Xidong Zhang, Qingjun Wang, Wenzheng Zhao, Baojun China Academy of Space Technology Shandong Institute of Space Electronic Technology Yantai264003 China Changfeng Electromechanical Technology Research and Design Institute Beijing100086 China Remote Sensing General Department of China Academy of Space Technology Beijing100086 China Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology School of Information and Electronics Beijing100081 China

Object detection in remote sensing images differs from general object detection in that it faces numerous challenges such as false alarm interference. However, conventional object detection methods primarily focus on scale and orientation variations, insufficiently addressing issues such as object blurring and false alarms, which frequently occur in large-scale Earth observation scenarios. To address this issue, this paper proposes an anti-interference object-detection algorithm based on mask guidance and adaptive margin loss, termed MG2ADet. Specifically, we devise a mask guidance module that leverages pseudo-mask labels to guide network features towards focusing on foreground regions, thereby enhancing the discriminability of object features and suppressing background noise interference. Besides, to further differentiate object and background features, we designed an adaptive margin classification loss function that guides the detection model in learning strongly discriminative features. This classification loss adaptively adjusts the angular margin penalty based on the image quality score of samples, assigning varying weights to different difficult samples, and ultimately improving the detection model's ability to discriminate between blurred objects and similar distractors. Extensive experiments on public remote sensing datasets demonstrated the excellent detection performance of our algorithm in comparison with numerous existing detectors (both one-stage and two-stage detectors). © 2008-2012 IEEE.

关键词： Object detection

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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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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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