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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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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

arXiv

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arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Novel Channel Error Calibration Algorithm for Multichannel High-Resolution and Wide-Swath SAR Imaging Based on the Oblique Projection

A Novel Channel Error Calibration Algorithm for Multichannel...

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

作者： Zhen Shi Jingtao Ma Lang Xia Yanyang Liu Yan Jiang Penghui Huang Qing Ling Guisheng Liao Xingzhao Liu School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China Shanghai Institute of Satellite Enginnering Shanghai China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The Azimuth multichannel synthetic aperture radar (SAR) can achieve the high resolution and wide swath (HRWS) imaging of the ground observation scene. However, the inevitable channel phase error will lead to azimuth ambiguities, i.e., the appearance of "ghost" points, which will degrade the SAR imaging quality. To address this issue, a novel channel error calibration method based on the oblique projection is proposed in this article. First, the optimal weight of each Doppler ambiguity component is calculated by the oblique projection. Then, the optimization function is constructed based on the power maximization method, from which the channel phase error can be estimated. Finally, the HRWS-SAR imaging can be precisely accomplished after performing the channel error calibration. The real-measured multichannel SAR data with azimuth ambiguity is provided to validate the proposed method.

关键词： Image resolution Azimuth Imaging Error compensation Channel estimation radar polarimetry Calibration Doppler effect Projection algorithms Synthetic aperture radar

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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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SELF-SUPERVISED DIFFUSION MRI DENOISING VIA ITERATIVE AND STABLE REFINEMENT

arXiv

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arXiv 2025年

作者： Wu, Chenxu Kong, Qingpeng Jiang, Zihang Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine USTC China MIRACLE Center Suzhou Institute for Advance Research USTC China State Key Laboratory of Precision and Intelligent Chemistry USTC China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology CAS China

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a "microscope" for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://***/FouierL/Di-Fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： signal to noise ratio

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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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Joint state-sharing and truncation BCJR for power-efficient coherent optical interconnections utilizing symbol-rate sampling DSP over a hollow-core fiber

引用

Optics Letters 2025年第10期50卷 3461-3464页

作者： Wang, Chenchen Li, Zhipei Dong, Ze Gao, Ran Song, Junyuan Mai, Tianle Guo, Dong Chang, Huan Pan, Xiaolong Zhu, Lei Xin, Xiangjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing100876 China

Power-efficient digital signal processing (DSP) architectures are indispensable for enabling the sustainable scalability of optical networks in next-generation data center interconnects (DCIs). This paper introduces a minimalist symbol-rate sampling (SRS) DSP framework for coherent optical systems, seamlessly integrated with a hollow-core fiber (HCF) to enable ultra-low-complexity signal processing. The core of this framework is the joint state-sharing and truncation Bahl–Cocke–Jelinek–Raviv (JSST-BCJR) algorithm, which enhances hardware efficiency by eliminating up to 91% of state-space computations, significantly reducing computational complexity while maintaining negligible performance degradation. Experimental validation confirms the feasibility of the proposed minimalist SRS-DSP framework through the successful transmission of a 100-Gbaud 16QAM signal over 20.6 km of HCF. This work underscores the viability of SRS-DSP in power-constrained DCIs and highlights the synergistic integration of low-complexity DSP and HCF, offering a pathway toward power-efficient, high-throughput optical networks. © 2025 Optica Publishing Group. All rights are reserved.

关键词： Network coding

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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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Impact of Air Quality on Millimeter Wave Transmission Performance 3

Impact of Air Quality on Millimeter Wave Transmission Perfor...

引用

3rd International Conference on Defence Technology, ICDT 2022

作者： Wu, Hongming Wang, Ningbo Wang, Yelong Wang, Jinkuan College of Information Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China

In this paper, we use millimeter wave radar to conduct outdoor experiments under different air quality conditions and analyze the degree of millimeter wave transmission attenuation. The radar we use operates at 94 GHz with a bandwidth of 1 GHz, a distance resolution of 15 cm, and a maximum detection distance of about 150 meters. We mainly use PM2.5 and PM10 indices as air quality criteria and find that the reflected signal strength of millimeter wave from 90m to 150m decreases by 0.38dB when the air quality rises from 136 to 250, indicating that the weaker the air quality, the weaker the millimeter wave signal strength. This provides an experimental basis for the field of millimeter wave communication. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Secure Communication Against Active UAV Eavesdropper: A Fingerprint-Localization and Channel Tracking Approach

引用

IEEE Transactions on Communications 2025年

作者： Wang, Xinyao Zheng, Zhong Fei, Zesong Wu, Qingqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China

Unmanned aerial vehicle (UAV) can be threatening to the information security of wireless communications. By launching the pilot spoofing attack (PSA), a UAV, operating as the active aerial-eavesdropper (A-Eve), is able to intercept the confidential messages sent over the air. On one hand, it is difficult to distinguish the channel state information (CSI) of the ground users (GUs) and the CSI of A-Eve in the contaminated pilots. On the other hand, due to the high-mobility of A-Eve, the CSI of A-Eve is rapidly changing, making the design of secure transmissions challenging. To address these issues, we first propose a location-based minimum mean square error (MMSE) channel estimation algorithm to separate the CSI of GUs and the CSI of A-Eve, where the location of A-Eve is obtained by designing a cooperative localization neural network (CLNet), leveraging its angular-domain channel fingerprint (CF) of A-Eve. Furthermore, we propose an artificial noise (AN) injected MMSE precoding scheme to maximize the worst-case secrecy rate of the multi-user communications, where the power allocation between signal and AN is optimized via a long short-term memory (LSTM)-based secure predictive beamforming neural network (SPBNet). Numerical results verify the secrecy performance gain of the proposed scheme achieved by utilizing the localization ability via the CLNet and the channel tracking ability via the SPBNet, compared to the canonical nullspace AN injection scheme without prior knowledge of A-Eve’s location. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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