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An improved 3-D reconstruction method based on deep neural network 14

An improved 3-D reconstruction method based on deep neural n...

引用

14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Zhang, Tianyi Ding, Zegang Liu, Siyuan Wei, Yangkai Wang, Guanxing Zhang, Yan Guo, Xin Gao, Yongpeng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China DFH Satellite Co. Ltd Beijing China

ISBN: (纸本)9783800758234

Current three-dimensional (3-D) reconstruction methods based on two-dimensional (2-D) inverse synthetic aperture (ISAR) image sequences usually consist of some sequential nonlinear steps, and they face with the error accumulation and transmission inevitably. To realize precise target reconstruction, an improved 3-D reconstruction method based on motion parameters and deep neural network (DNN) is proposed. The proposed method could realize the end-To-end transformation from the motion parameters to the 3-D target via DNN, and the error transmission and accumulation can be avoided. Results based on the synthetized data set validate the proposed method. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep neural networks

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An Adaptive Estimation Method for Group Target Number Based on information Theoretic Criterion

An Adaptive Estimation Method for Group Target Number Based ...

引用

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

作者： Libin Dou Qi Jiang Rui Wang Mengxin Shi Jichuan Zhang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The number of individuals within a group is crucial information for the correct association, robust tracking, and situation awareness. Traditional methods using the range profile’s peaks are not suitable when targets are densely distributed. The target number estimation algorithms based on monopulse systems have high computational complexity, while those based on array systems require additional sensor resources. To address these issues, this paper proposes a method based on multiple incoherent observations using one monopulse radar. By combining multiple incoherent observations from a single sensor with information theoretic criterion, this approach can estimate the number of targets even when the resolution of a single sensor is insufficient. The effectiveness of the algorithm is verified through simulations, and the algorithm's performance is analyzed.

关键词： Analytical models Target tracking Estimation Radar Radar tracking Data processing Adaptive estimation Computational complexity Signal resolution Sensor arrays

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Labeled Random Finite Sets-Based Group Target Tracking with Multi-Detection Model

Labeled Random Finite Sets-Based Group Target Tracking with ...

引用

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

作者： Longxiang Jiao Qi Jiang Rui Wang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar tracking applications for low-altitude group targets (such as bird flocks or drone swarms), the traditional point target measurement model is often unsuitable due to the complex micro-Doppler signatures and multipath effects. Algorithms based on labeled random finite sets (RFS) challenges with multi-detection problems. This paper presents a new method for tracking low-altitude group targets using labeled RFS in radar systems. To mitigate the disturbance from multi-detection, we propose modified assignment cost matrices in the update process, which reduces the number of false associations, and thus achieves quality enhancement of trajectory estimations. The effectiveness of the proposed method is validated through simulation results.

关键词： Radio frequency Target tracking Costs Simulation Estimation Radar Radar tracking Trajectory Reliability Standards

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Improved multiple hypothesis tracking using incomplete measurements for group targets

Improved multiple hypothesis tracking using incomplete measu...

引用

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

作者： Na Ni Qi Jiang Cheng Hu Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Tracking targets within a group has received much attention in the radar field recently years. Group targets are usually closely spaced with similar velocity, making it hard to distinguish multiple targets. For precise target tracking, the tracking radar usually employs a narrow beam to achieve the high range-angular resolution. However, targets may enter and exit the narrow beam frequently during the observation, bringing difficulty for stable tracking. Moreover, it is also hard to distinguish different targets when their tracks intersect on the range-doppler plane. These kinds of incomplete measurements increase the difficulty of group targets' association and tracking. Therefore, this paper proposes an improved multiple hypothesis tracking method using incomplete measurements. The adaptive detection probability model and track intersecting probability are combined in the multiple hypothesis tracking framework to reduce false associations and improve tracking performance. Finally, simulation and experiment results verified the effectiveness of the proposed method.

关键词： Adaptation models Target tracking Accuracy Radar Radar tracking Data processing Data models

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Uncertainty-Aware Dynamic Learning for Cross-Domain Few-Shot Scene Classification from Remote Sensing Imagery

Uncertainty-Aware Dynamic Learning for Cross-Domain Few-Shot...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Can Li He Chen Yin Zhuang Shanghang Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Computer Science Peking University Beijing China

Cross-domain few-shot scene classification (CDFSSC) is devoted to transferring knowledge from the source domain to the target domain and facilitating few-shot classification for the target domain. However, due to the domain shifts between source and target domains, high uncertainty would be generated in the knowledge transfer process, leading to unreliable cross-domain learning, which degenerates classification performance on the target domain severely. Thus, in this paper, aiming to reduce the interference of high uncertainty and improve the reliability of cross-domain knowledge transfer, a novel uncertainty-aware dynamic learning (UDL) framework is proposed for CDFSSC from remote sensing imagery. First, a mean-teacher architecture combining pseudo-labeling and consistency regularization is utilized to achieve cross-domain learning. Second, a UDL strategy is proposed to divide data into positive and negative samples based on a well-designed uncertainty-aware dynamic threshold, conducting positive and negative learning respectively, to advance a more reliable knowledge transfer. Third, to further improve cross-domain capability, a self-entropy loss is designed to reduce the epistemic uncertainty of the model. Extensive experiment results indicate the superiority of our proposed methods.

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A Long-Term Joint Multi-Image Computerized Ionospheric Tomography Method Based on GEO SAR System

A Long-Term Joint Multi-Image Computerized Ionospheric Tomog...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Sui Xichao Dong Yuanhao Li Zhiyang Chen Cheng Hu Beijing Key Laboratory of Embedded Real-Time Information Processing Technology (Beijing Institute of Technology) Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Computerized Ionospheric Tomography (CIT) serves as a crucial method for ionospheric monitoring, playing a significant role in space environment surveillance and earthquake prediction. Existing CIT techniques are mostly based on the Global Navigation Satellite System (GNSS) system, constrained by the distribution of receivers. CIT based on geosynchronous SAR (GEO SAR) presents a solution by leveraging Persistent Scatterer (PS) points within the scene. However, current CIT techniques using GEO SAR typically utilize PS points from a single SAR image. This paper introduces a novel approach – a GEO SAR-based long-term joint multi-image CIT method. This method enhances the exploitation of satellite data, offers a broader range of observation angles, and improves tomography accuracy. Finally, through a CIT experiment involving three GEO SAR images, the electron density distribution is derived with a time resolution of 10 minutes. The results align with the International Reference Ionosphere (IRI) data, validating the feasibility and advantages of the proposed method. It is noteworthy that the proposed method caters to the tomography observation mode of a single satellite and is adaptable to each satellite within a satellite formation.

关键词： Global navigation satellite system Satellites Accuracy Surveillance Receivers Tomography Radar polarimetry

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An improved ISAR imaging method under low SNR 14

An improved ISAR imaging method under low SNR

引用

14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Gao, Yongpeng Ding, Zegnag Zhang, Tianyi Liu, Shujiang Guo, Xin Li, Pucheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401147 China DFH Satellite Co. Ltd Beijing100094 China

ISBN: (纸本)9783800758234

Current inverse synthetic aperture radar (ISAR) imaging methods cannot perform well when the signal-To-noise ratio (SNR) is low. In this paper, an improved ISAR imaging method based on generalized Radon-Fourier transform (GRFT) and relaxation (RELAX) technique is proposed. Through the proposed method, a well-focused radar image of moving target under low SNR condition can be obtained. Further, the estimation error caused by off-grid points as well as the unnecessary side lobe and main lobe values introduced by GRFT based ISAR imaging can be solved. Simulations are conducted to verify the effectiveness of the proposed method. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Inverse synthetic aperture radar

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects

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

引用

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

作者： Mingchen Yuan Jiaqi Liu Xinyang Wang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Zhengzhou Academy Of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Manifolds Training Sensitivity Accuracy Target recognition Prototypes Training data Radar Data processing

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Missing Modality Completion for Multi Frequency Radar HRRP Recognition Using GAN

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

引用

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

作者： Qiang Zhou Bingqian Yu Yuanhao Meng Xin Zhang Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing 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

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.

关键词： Training Frequency locked loops Target recognition Fuses Radar Interference Feature extraction Generative adversarial networks Data processing Testing

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Multi-Terrain Radar HRRP Target Detection Using Contrastive Learning

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

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

作者： Xinyang Wang Mingchen Yuan Jiaqi Liu Yang Li Yanhua Wang Liang Zhang Radar Technology Research Institute Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

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.

关键词： Training Energy resolution Radar detection Object detection Contrastive learning Radar Detection algorithms Clutter Signal to noise ratio Testing

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