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IEEE International Conference on information Communication and signal processing (ICICSP)

作者： Hui Fang Qingping Meng Guisheng Liao Yongjun Liu Cao Zeng Zhuochen Chen National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Synthetic aperture radar (SAR) image registration contributes significantly to various applications of SAR images. Conventional scale invariant feature transform (SIFT) alignment methods possess abysmal robustness for SAR image registration resulting from speckle noise in SAR images. Attributing to the excellent feature representation ability of convolutional neural networks (CNN), a simple and effective deep registration algorithm for SAR images is proposed in this paper. Specifically, we construct a CNN to extract the features of SAR images and to accomplish the feature matching, while outputting the transformation matrix for SAR image registration directly. Furthermore, a data generation strategy is adopted to solve the problem of insufficient training data during the training process. Experiment results on the real data verify the effectiveness of the proposed method.

关键词： Training Training data signal processing algorithms Transforms Data collection Speckle Feature extraction radar polarimetry Convolutional neural networks Synthetic aperture radar

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Knowledge-Aided Covariance Matrix Estimation Algorithm Based on Geometric Mean in STAP for Airborne radar

Knowledge-Aided Covariance Matrix Estimation Algorithm Based...

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IET International radar Conference 2023, IRC 2023

作者： Jing, Yang Du, Xiaolin Cui, Guolong Zheng, Jibin Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781839539954

The problem of knowledge-aided (KA) covariance matrix estimation in airborne radar space-time adaptive processing (STAP) is studied in this paper. In the traditional KA covariance matrix estimation methods, the insufficient number of available samples and inaccurate prior knowledge can cause serious deterioration of STAP performance. To tackle this problem, a new KA covariance matrix estimation algorithm based on geometric mean is proposed. Specifically, by utilizing the persymmetric structure information as well as the geometric mean in the space of positive definite matrices, a geometric mean estimator is constructed, which is then linearly combined with the prior covariance matrix to obtain a new type of CCM estimators. Simulation results illustrate that the proposed algorithm has good STAP performance with small samples and inaccurate prior knowledge. © The Institution of Engineering & Technology 2023.

关键词： Covariance matrix

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A Cross-domain PAPR Reduction Method for OTFS Modulation

A Cross-domain PAPR Reduction Method for OTFS Modulation

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IEEE International Conference on Communications in China (ICCC)

作者： Dongkai Zhou Siqiang Wang Zhong Zheng Jing Guo Zesong Fei Weihua Yu School of Information and Electronics Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

In this work, a cross-domain PAPR reduction scheme is proposed, which is a combination of the phase rotation in the Delay-Doppler (DD) domain and the iterative clipping filtering (ICF) in the time domain. The former technique is distortion-less to the signals while incurring additional signaling overhead between the transceivers. The latter technique is more effective for PAPR reduction, but it can cause increased signal detection errors. Therefore, by leveraging the advantages of the two techniques, the proposed scheme can achieve improved PAPR reduction, given a trade-off between the signaling overhead and the bit error rate (BER). Specifically, the phase-rotation vectors optimization problem in the DD domain is formulated, and an iterative algorithm based on successive convex approximation (SCA) method and penalty convex-concave procedure (PCCP) is employed to solve this non-convex problem. Considering the phase quantization and signaling overhead in practical systems, a low-overhead group randomization algorithm is proposed to select the phase mapping in the grouped phase rotation stage, while the linear filtering is applied in the ICF stage, which can flexibly adjust the signaling overhead and the BER. Compared to the existing methods, numerical simulations justify the advantages of the proposed scheme, which further reduces 1-3 dB PAPR, while causing a lower BER.

关键词： Time-frequency analysis Filtering Simulation Bit error rate Peak to average power ratio Transceivers Iterative methods

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DCTracker: Differential Motion Compensation Based Multi-Object Tracking in Satellite Videos

DCTracker: Differential Motion Compensation Based Multi-Obje...

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

作者： Yuting Shi Yin Zhuang He Chen Miaoxin Cai Jianlin Xie Shuyu Gan National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP) Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Multi-object tracking (MOT) in satellite videos is a vital technique in Earth observation, serving various purposes such as smart city management and military target reconnaissance. However, satellite videos face several challenges, including wide observation ranges, low target pixel proportions, and significant background interference such as cloud drift and lighting changes. Relying solely on appearance features from single-frame videos is insufficient for fully identifying targets; extracting target motion information across multiple frames is also necessary for *** paper introduces an innovative integrated real-time architecture for multi-object tracking, named DCTracker. Specifically, to address the loss of target motion information and the issue of weak targets being easily obscured by noise, we designed a differential compensation module to enhance discrimination between targets and noise by analyzing the motion characteristics of targets across consecutive video frames. Experimental results show that DCTracker achieves advanced performance, with an accuracy score of 61.3% on the VISO dataset, demonstrating its effectiveness in handling complex tracking scenarios.

关键词： Target tracking Satellites Smart cities Noise Semantics Reconnaissance Feature extraction Spatiotemporal phenomena Remote sensing Videos

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A tracking filter algorithm for migratory birds based on group situation constraints

A tracking filter algorithm for migratory birds based on gro...

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

作者： Biao Li Rui Wang Jiong Cai The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Aerial animal migration is closely related to human life, and radar is one of the most effective means of observing it. However, scanning radar has a low data rate, a short integration time, and lacks prior knowledge of target speed, while migratory animals exhibit high mobility. These factors make traditional tracking filter algorithms ineffective at predicting their movement. To address this issue, this paper proposes a tracking filter algorithm based on group dynamics constraints, leveraging the characteristic behavior of migratory animals flying in groups. First, the dynamic state of the group is calculated using the group as the research unit. Then, tracking filtering is performed under the constraint of group dynamics. Simulation and field data have verified the effectiveness of the proposed algorithm.

关键词： Target tracking Heuristic algorithms Dynamics radar Filtering algorithms radar tracking Prediction algorithms Birds information filters radar signal processing

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First Analysis of PIESAT-01 Interferometric Cartwheel for Ocean Current Measurements

First Analysis of PIESAT-01 Interferometric Cartwheel for Oc...

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

作者： Huangjiang Fan Zhibing Wang Qingjun Zhang Lei Liu Huancheng Guo Tao Wang Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The World's First Multi-Baseline Distributed InSAR Satellite System ‘PIESAT-01’, which has the ability of Along-track interferometric (ATI), and can simultaneously form multiple effective orbital baselines to obtain the ocean current. This article presents the results of ocean current processing based on PIESAT-01 IW data for the first time, and introduces the data processing flow and the used processing method.

关键词： Satellites Oceans Current measurement Data processing Orbits

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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A Novel Multi-subaperture Motion Compensation Algorithm Based on Extended Quadratic Error Model for UAV SAR Imaging

A Novel Multi-subaperture Motion Compensation Algorithm Base...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Weidi Xu Pengjie You Xiaoliang Yang Lin Wang Shuo Feng The 54th Research Institute of CETC Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has been widely used in modern remote sensing applications. The requirements for high-resolution and real-time processing have grown rapidly in recent years. In practice, affected by flight deviations, motion errors are inevitable in returned echoes, leading to residual range cell migration (RCM) error and azimuth phase error (APE) at the same time. The SAR image formation algorithm in the frequency domain is considered efficient. While some operations, such as RCM correction, can break the linear relationship of errors between the range and azimuth direction, making motion compensation complicated. This paper derived the relationship between the residual RCM and APE based on the quadratic error model and proposed a novel multi-subaperture motion compensation (MOCO) method, which can be applied to high-order errors. Simulations are provided to verify the correctness and effectiveness of the method.

关键词： Adaptation models Azimuth Frequency-domain analysis Autonomous aerial vehicles Motion compensation radar polarimetry Real-time systems

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Learning Interpretable Phase Difference Mapping for Scalable DOA Estimation via Deep Learning

Learning Interpretable Phase Difference Mapping for Scalable...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Shulin Hu Cao Zeng Minti Liu Guisheng Liao Hangzhou Institute of Technology Xidian University Hangzhou China National Key Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Interpretability is the major bottleneck regarding the deep learning-based direction of arrival estimation (DL-DOA). This paper provides novel insight into the mapping relationship (MR) for DL-DOA, which can be interpreted as a mapping from the received signal to the spatial phase difference (SPD) with prior information of array geometry (AG). Based on the learned MR, two scalable DL-DOA methods are developed. One method refers to the variability of AG, enabling the trained model to be utilized for varying AGs without the requirement of retraining network. The other involves scaled resolution (SR) by using an equivalent finer grid, which significantly mitigates the problem of grid mismatch for off-grid DOA with a coarse pre-defined grid. The efficacy of proposed methods is demonstrated through simulations with different network structures. The code for this work is available at: https://***/hslcode/DOA-Scalable.

关键词： Phased arrays Geometry Training Deep learning Direction-of-arrival estimation Conferences Estimation Parallel processing Spatial resolution signal resolution

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A Hardware Implementation of Spaceborne SAR BP Algorithm Based on Precise Region Focusing and Hardware-Software Collaboration

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 14160-14174页

作者： Li, Yongrui Chen, Liang Zheng, Pengnan Ding, Jie Zhang, Zhihan Yang, Zhu Xie, Yizhuang Li, Han Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology Zhuhai519088 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China

Onboard imaging of Spaceborne Synthetic Aperture radar (SAR) is a key technology in the field of high-timeliness remote sensing applications. It adopts the hardware platform deployed on the satellite to convert the SAR radar echoes into visual images. Combined with image detection and recognition, it can rapidly downlink and distribute the effective information. At present, high-resolution and wide-swath imaging is the development trend of spaceborne SAR imaging. The Back Projection (BP) algorithm is an excellent high-resolution imaging algorithm for spaceborne SAR, but it demands large storage resources and involves heavy computations. Given the limited resources of the satellite's hardware platform, the traditional hardware implementation of the algorithm can hardly support large-scale scenes imaging. Even if it can, the imaging time is extremely long. This paper proposes an improved Cartesian Factorized Back Projection (CFBP) algorithm based on precise region focusing. By dividing the imaging sub-regions and using a stepped data reading method, the demand for storage can be significantly reduced. Moreover, a software-hardware collaboration method based on scheduling acceleration is put forward. Through constructing an ultra-long projection pipeline, the continuity and speed of the algorithm execution can be enhanced. According to the proposed method, a Projection Intellectual Property (IP) and a complete imaging system are constructed using the Xilinx VU9P Field Programmable Gate Array (FPGA) board. Experimental results show that the Projection IP can save up to 40.78% - 85.09% of Random Access Memory Blocks (BRAM) , and the projection calculation time can be reduced by up to 54.54%. The complete imaging system can process the improved CFBP projection algorithm for images of sizes 32K × 32K, 16K × 16K, and 8K × 8K within 2308.11 seconds, 598.07 seconds, and 167.81 seconds, respectively. Compared with other FPGA-related research, it supports large-size scene imagi

关键词： Random access storage

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