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LT-1 Differential Tomography: First Results and Comparison between Monostatic and Bistatic Systems

LT-1 Differential Tomography: First Results and Comparison b...

引用

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

作者： Shimeng Zhou Zhiyang Chen Yuanhao Li Cheng Hu Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Advanced Technology Research Institute 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

Differential Tomography SAR technology integrates the advantages of Differential Interferometric SAR and Tomographic SAR, enabling four-dimensional imaging in areas with SAR layover to obtain elevation and deformation information of the target area. Compared to the traditional monostatic mode, the space-based bistatic observation mode has the advantages of interference immunity and richer information acquisition. This paper uses Differential Tomography SAR technology to perform deformation inversion on the LuTan-1 monostatic and space-based bistatic data. While obtaining the elevation information and linear deformation rate of the observed scene, it also compares the results and performance of Differential Tomography under the two different *** final conclusion is that LuTan-1 demonstrates strong capabilities in differential tomography. Additionally, the differential tomography performance of space-based bistatic systems is superior to that of monostatic systems.

关键词： Point cloud compression Accuracy Deformation Buildings Interference Tomography Data processing Root mean square Standards Monitoring

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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...

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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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Radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

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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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Robust Adaptive OS-CFAR Detector for Maneuvering Group Target Based on Formation Prior Knowledge

Robust Adaptive OS-CFAR Detector for Maneuvering Group Targe...

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

作者： Mengxin Shi 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

Detecting the individuals such like the bird flocks or a UAV swarm is a great challenge for radar systems. The lack of prior knowledge about the characteristics of dynamic group targets leads to the performance degradation of conventional detectors. In this study, we present a robust adaptive OS-CFAR detector for maneuvering group targets, leveraging prior knowledge of their formation. The group target’s state is updated and predicted using the random matrix approach. The projection length of the group target in the radar beam direction is computed using the predicted information during the group target tracking process. Using the projection length, the number of reference units occupied by the group target within the reference window is determined, and this prior knowledge is applied to update the detection parameters of the OS-CFAR detector. The simulation result shows that the algorithm can update the detection parameters timely and avoid target missed detection.

关键词： Target tracking Simulation Radar detection Detectors Radar Radar tracking Prediction algorithms time measurement real-time systems Motion measurement

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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 ...

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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 ...

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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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HIGH-RESOLUTION RADAR GROUND CLUTTER GENERATION BY DENOISING DIFFUSION PROBABILITY MODEL

HIGH-RESOLUTION RADAR GROUND CLUTTER GENERATION BY DENOISING...

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IET International Radar Conference 2023, IRC 2023

作者： Yu, Bingqian Zhou, Qiang Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Lab 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 Shandong250300 China

ISBN: (纸本)9781839539954

Environmental clutter significantly impacts the performance of High-Resolution Range Profiles (HRRP) in detection and recognition. Hence, it's crucial to model and simulate radar clutter for effective radar system design. However, traditional methods are limited to fixed probability distributions, struggle to accurately replicate clutter in complex real-world scenarios. In this paper, we propose to generate clutter based on the Denoising Diffusion Probabilistic Model (DDPM). DDPM can learn the distribution of training data through its probabilistic modelling method, solving the limitations brought by traditional methods. In addition, in order to evaluate the amplitude characteristics of generated clutter, we propose to compare the fitting results of specific physical probability distributions of generated clutter with those of real clutter, as well as evaluate the distribution distance with real clutter. Experimental results on measured clutter data show that the clutter synthesized by DDPM can well match the distribution of the measured clutter. And it is able to demonstrate good performance in different scenarios. © The Institution of Engineering & technology 2023.

关键词： Clutter (information theory)

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Improved multiple hypothesis tracking using incomplete measurements for group targets

Improved multiple hypothesis tracking using incomplete measu...

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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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FOUNDATION PIT DEFORMATION MEASUREMENT TECHNIQUE BASED ON GROUND-BASED INTERFEROMETRIC RADAR

FOUNDATION PIT DEFORMATION MEASUREMENT TECHNIQUE BASED ON GR...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Changjun Zhou, Hanpu Wang, Jian Gao, Jiake Deng, Yunkai Tian, Weiming Beijing Building Research Institute Co. Ltd. CSCEC Beijing100076 China Radar Research Lab School of Information and Electronics Bejing Institute of Technology Bejing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

The deformation monitoring data of foundation pit is an important criterion for forecasting and strengthening measures in the field of building safety. During the construction period, the foundation pit will be affected by load, temperature change, random disturbance and other effects to produce deformation. Due to the complex deformation process of foundation pit, it is hard to accurately describe and separate the load trend deformation and elastic thermal deformation by mathematical expression. Excessive thermal deformation may cause cracks in the surrounding pavement and affect the judgment of load deformation. This paper first introduces the deformation measurement method of foundation pit based on differential interferometry technology using ground-based interferometric radar. Based on the obtained deformation type, the simulation verifies the separation effect of wavelet transform on different deformation trends and periodic components. Through the foundation pit deformation monitoring experiment of Tongzhou infrastructure project, the wavelet transform method is used to decompose the foundation pit deformation curve, and the extracted periodic component has good consistency with the temperature information. © The Institution of Engineering & technology 2023.

关键词： Wavelet decomposition

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Orbit Determination Error Analysis and Compensation for Multi-static GEO SAR Imaging

Orbit Determination Error Analysis and Compensation for Mult...

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IET International Radar Conference 2023, IRC 2023

作者： Liu, Yan Zhang, Wentao Chen, Zhiyang Dong, Xichao Huang, Xiaotao Chen, Leping School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100084 China Beijing Institute of Remote Sensing Equipment Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781839539954

The high orbit altitude of Geosynchronous Synthetic Aperture Radar (GEO SAR) introduces a significant challenge of long synthetic aperture times, which restricts the application of rapid imaging. A multi-static GEO SAR system consisting of multiple satellites flying along track can be employed to jointly image, and thus, to reduce the synthetic aperture time, but the orbit determination errors of different satellites will bring serious defocusing effect to the sub-image synthesis. In this paper, the effects of orbit determination errors on sub-image offset, defocusing and constant phase errors are analyzed in the multi-static GEO SAR system, and an orbit determination error correction method based on phase gradient autofocus (PGA) and interferometric phase estimation is proposed. Simulation results validate the effectiveness of the method. © The Institution of Engineering & technology 2023.

关键词： Error compensation

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