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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Yunfeng Liu Tianyi Car Xiaonan Wu Tong Gu Linglong Zhang Yachao Li HangZhou Institute of Technology Xian University HangZhou China Institute of the second Academy of Aerospace Science and industry Beijing China National Key Laboratory of Radar Signal Processing Xian University Xian China

This article proposes a novel post-processing method for ship detection in synthetic aperture radar (SAR) images, which addresses the issue of head to tail separation that cannot be avoided when using morphological compensation to process candidate regions generated by the constant false alarm detection (CFAR). This algorithm first constructs a connected domain for the candidate regions generated by CFAR, uses a greedy strategy to update the centroid and size of the connected domain, and finally unreasonable candidate connected domains are removed to eliminate high noise sea clutter and large volume non ship targets. Compared to the morphological compensation post-processing method, the greedy strategy the greedy strategy fully utilizes the prior knowledge of ship data in the post-processing of constant false alarm ship detection results, overcomes the disadvantage of complex morphological compensation methods that require fine-tuning parameters in post-processing, and completely solves the problem of head tail separation that cannot be completely solved, improving the accuracy of synthetic aperture radar ship detection. The effectiveness and robustness of our method have been demonstrated by the data from the domestically produced Gaofen 3 satellite and the data from the European Space Agency's TerraSAR-X satellite.

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High Degree of Freedom Low Coupling Sparse MIMO Array

High Degree of Freedom Low Coupling Sparse MIMO Array

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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Yichang Yao Baohe Pang Yingsong Li Canping Yu Wudang Xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

To achieve high precision direction-of-arrival estimation of MIMO arrays in highly coupled environments, based on the difference and cooperative array principles, the uniform array fitted with 5 base layer array and the uniform array fitted with 6 base layer array were used as transmitting arrays, and with the addition of 1 base layer layer sparse array as receiving array, two sparse multi-in multiple-out arrays were designed for single-base radar, which have the characteristics of low coupling of transmitting array and high freedom of receiving array.

关键词： Couplings Direction-of-arrival estimation Accuracy Target recognition Fitting Estimation radar

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Extended Coprime Arrays with Sum-Difference Coarray for DOA Estimation 5

Extended Coprime Arrays with Sum-Difference Coarray for DOA ...

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Xin, Wei Xu, Lin Gao, Jincheng Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Wuhan Second Ship Design and Research Institute Wuhan China Zhaojin Mining Industry Co. Ltd. Zhaoyuan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A modified coprime array (CA) with a name of extended CA (ECA) is presented and constructed to reduce over-lapping sensors between sum and difference coarray using conventional CA (CCA) to get a sum-difference coarray with high degrees- of-freedoms (DOFs). The devised ECA is to shift a sub array of CCA to reduce redundancy and then to flip part of shifted sub array around zero point that is regarded as symmetric center. The onstructed ECA expands the inter-element separations with lower mutual coupling. Numerical simulation are provided to verify superiority of the devised ECA. © 2022 IEEE.

关键词： Direction of arrival

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Design of A 110-GHz On-Chip Antenna-Coupled Power-Adjustable Tripler based on Schottky Diodes

Design of A 110-GHz On-Chip Antenna-Coupled Power-Adjustable...

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IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Ziru Chen Xiang Gao Dan Qiao Huanxin Li Heng Liu Xiangyuan Bu Jianping An School of Information and Electronics Beijing Institute of Technology Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

In this paper, we present the design of a 110-GHz on-chip antenna-coupled power-adjustable tripler based on the Schottky diodes. The design integrates two frequency triplers, on-chip antenna, and power synthesizing circuit on one single substrate. The input Ka-band signal is evenly divided via a power splitter into two signals, whose phase difference is adjusted with a phase shifter. Those two Schottky-diodes-tripled signals are synthesized by a 3-dB 90° coupler, and the output power is controllable by adjusting the phase difference between two signals after the power splitter. The simulation results show that the output power can be adjusted from -5 dBm to 17.67 dBm across - 25° to 85° phase difference range.

关键词： Radio frequency Schottky diodes Simulation Phase shifters Receivers System-on-chip Ka-band Antennas Power generation Substrates

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Improved Clutter Suppression Method for Bridge Vibration Measurement Based on GB-MIMO radar

Improved Clutter Suppression Method for Bridge Vibration Mea...

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

作者： Jiake Gao Jian Wang Siqi Wang Xinran Qu Wenyu Li Yunkai Deng Radar Technology Research Institute Bejing Institute of Technology Beijing China Corporation Limited of CSCEC Beijing Building Research Institute Beijing China Beijing Key Laboratory of Embedded Realtime Information Processing Technology Radar Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Ground based multi-input multi-output (GB-MIMO) radar has been gradually applied in the field of bridge vibration monitoring. During measurement, the interference of static clutter on the vibrating target signal in the same azimuth and range resolution severely affects the vibration measurement. The traditional method of removing static clutter is to estimate static clutter by fitting a circular curve to a complex signal point set, that is, fitting the vector of the circular curve center deviating from the origin of the polar coordinate system. Due to the clustered distribution of vibration points during non vibration time, the trajectory of complex sequence point sets will be distorted, resulting in poor clutter suppression effect. This article proposes an improved method for measuring the dynamic deflection of bridges by suppressing clutter. Firstly, obtain the vibration time period of the vibration candidate points through time-frequency analysis. Secondly, extract the complex sequence of each pixel during the vibration time period, and use the circular trace fitting method to estimate the stationary clutter. Then, based on outlier degree and outlier rate evaluation indicators, select vibration measurement pixels that can achieve effective clutter suppression. The effectiveness of this method was preliminarily verified by comparing the dynamic deflection curves of millimeter wave radar and laser vibrometer through model bridge experiments. In addition, the method was also validated through a high-speed railway bridge experiment.

关键词： Vibrations Bridges Time-frequency analysis Laser radar Structural panels radar measurements Fitting Vibration measurement Trajectory Clutter

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Ship detection based on the modified YOLOV5 for SAR imagery

Ship detection based on the modified YOLOV5 for SAR imagery

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2024 International Conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Wang, Zhongbo He, Miao Ding, Qinghai Luo, Haibo Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Space Star Technology Co Ltd. Beijing100086 China

ISBN: (纸本)9781510680425

Synthetic aperture radar (SAR) is an essential tool for ocean *** the main participants on the ocean, ships are the most important targets for ocean *** it is of great importance to develop ship detection algorithms for SAR sea *** algorithms based on Convolutional Neural Network perform far better than the traditional methods based on manual features on ship detection task due to the powerful feature representation *** algorithms based on Convolutional Neural Network can be divided into one-stage algorithms, and two-stage ***-stage algorithms have high accuracy, but are relatively ***-stage algorithms have high inference speed, but compared with two-stage algorithms, they have lower *** in this article, we proposed an modified one-stage detection algorithm to improve the accuracy of ship detection in a condition that the modified algorithm meet the real-time ***, the small model of one-stage algorithm YOLOV5 is chosen as the base network to get the high inference ***, to improve the accuracy of ship detection with a little increase in inference time and the model parameters, we integrate the one-layer super-resolution architecture with the simplist structure into the YOLOV5 ***, we conducted the comparative experiments on our Dataset to verify the performance of modified YOLO *** experimental results show that the modified method has obtained an Average Precision (AP) improvement than the original YOLO V5 for detecting ships in SAR images with a little increase in inference time and the model parameters. © 2024 SPIE.

关键词： Convolutional neural networks

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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Haolin Xia Sirui Sun Xinyu Liu Ju Zhou Hanpu Wang Tong Chen School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data.

关键词： Dimensionality reduction Automation Anxiety disorders Feature extraction Physiology Stress

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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Efficient Robust Principal Component Analysis via Block Kryl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shun Fang Zhengqin Xu Shiqian Wu Shoulie Xie School of Information Science and Engineering Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Signal Processing RF & Optical Dept. Institute for Infocomm Research A*STAR Singapore

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.

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Algorithm for Multipath Interference Restraint Based on Blind Source Separation in Passive GNSS-Based Bistatic radar 1

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6th EAI International Conference on IoT as a Service, IoTaaS 2020

作者： Wen, Yuanyuan Bai, Lin Zhang, Xi Shang, She Song, Dawei Guo, Shuai National Key Laboratory of Science and Technology on Space Microwave Xi’an710100 China Academy of Space Information Systems Xi’an710100 China National Laboratory of Radar Signal Processing Xidian University Xi’an710070 China

ISBN: (数字)9783030675141

ISBN: (纸本)9783030675134

Passive bistatic radar belongs to passive radar systems and is a variant of bistatic radar that exploit non-cooperative‘illuminators of opportunity’ as their sources of radar transmission. Passive bistatic radar has a lot of advantages such as double-base system, silent acceptance, inherently low cost and so on, and hence attractive for a broad range of applications in recent years. The Passive GNSS-Based bistatic radar system exploits Global Navigation Satellite System as the illuminators of opportunity to detect the potential targets and is inevitable affected by multipath interference due to the reflection effect of mountains and near-earth buildings. If reference signal containing multipath interference is directly used for matching filter processing, the range-Doppler diagram will show the false target formed by matching multipath interference with echo signal. In this paper, a novel blind source separation method is proposed to recovering multipath interference from reference channel data in Passive GNSS-Based bistatic radar. The elementary reflection matrix is used as a rotation matrix to transform the cumulant matrix to realize the purpose of diagonalization. Finally the direct wave signal and multipath interference signals were separated successfully. Both theoretical analysis and simulation result verify multipath interference can be well suppressed by the proposed method. © 2021, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Blind source separation

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Deep Uncertainty-aware Tracking for Maneuvering Targets

arXiv

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arXiv 2024年

作者： Zhang, Shuyang Gao, Chang Zhang, Qingfu Jia, Tianyi Liu, Hongwei The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have overlooked measurements’ confidence, markedly escalating the challenge of fitting a mapping from measurement sequences to target state sequences. To address these issues, this paper presents a deep maneuvering target tracking methodology based on target state space projection. The proposed methodology initially establishes a projection from the target measurement sequence to the target state space by formulating the probability density function of measurement error and samples the distribution information of measurement noise within the target state space as a measurement representation. Under this representation, the sequential regression task of target state estimation can be transmuted into a task of detecting the target location in the state space. Subsequently, a deep detection network is devised to accomplish target location detection in the target state space. Finally, a loss function is designed to facilitate the network’s training for attaining the desired network performance. Simulation experiments suggest that the proposed method can maintain satisfactory tracking performance even when the target maneuvers, and can rapidly converge and achieve higher estimation accuracy compared with existing methods after the target maneuvers. Copyright © 2024, The Authors. All rights reserved.

关键词： Probability density function

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