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3rd International Conference on Mechatronics Engineering and information Technology(ICMEIT 2019)

作者： Yong Qin Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology School of Information Engineering Nanchang Institute of Technology

This paper proposes a novel box-particle multipath cardinality balanced multitarget multiBernoulli(BP-MPCBMeMBer) filter for the problem of multitarget tracking in Over-the-horizon radar(OTHR). The proposed algorithm combines the multipath cardinality balanced multitarget multiBernoulli(MPCBMeMBer) filter with interval analysis to solve the problems of high computational complexity and nonlinear measurement models. First, establishment of the OTHR measurement model based on interval analysis, and then the box-particle filter is used to derive the BPMPCBMeMBer based on the original MPCBMeMBer filter. Eventually, the simulation results are presented to verify the effectiveness of the BP-MPCBMeMBer filter.

关键词： Box-particle filter CBMeMBer filter OTHR Tracking

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One-Bit target detection in collocated MIMO radar and performance degradation analysis

arXiv

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arXiv 2020年

作者： Xiao, Yu-Hang Ramírez, David Schreier, Peter J. Qian, Cheng Huang, Lei The College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China The Department of Signal Theory and Communications Universidad Carlos III de Madrid Madrid28903 Spain The Gregorio Marañón Health Research Institute Madrid28007 Spain The Signal and System Theory Group Paderborn University Paderborn33098 Germany The IQVIA Inc. CambridgeMA02139 United States The Guangdong Key Laboratory of Intelligent Information Processing The Shenzhen Key Laboratory of Advanced Navigation Technology Shenzhen University Shenzhen518060 China

Target detection is an important problem in multiple-input multiple-output (MIMO) radar. Many existing target detection algorithms were proposed without taking into consideration the quantization error caused by analog-to-digital converters (ADCs). This paper addresses the problem of target detection for MIMO radar with one-bit ADCs and derives a Rao's test-based detector. The proposed method has several appealing features: 1) it is a closed-form detector;2) it allows us to handle sign measurements straightforwardly;3) there are closed-form approximations of the detector's distributions, which allow us to theoretically evaluate its performance. Moreover, the closed-form distributions allow us to study the performance degradation due to the one-bit ADCs, yielding an approximate 2 dB loss in the low-signal-to-noise-ratio (SNR) regime compared to ∞-bit ADCs. Simulation results are included to showcase the advantage of the proposed detector and validate the accuracy of the theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： Analog to digital conversion

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Hepatic texture synthesis method based on the accelerated CUDA 14th

Hepatic texture synthesis method based on the accelerated CU...

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14th International Conference on intelligent information Hiding and Multimedia signal processing, IIH-MSP 2018

作者： Xu, Lin Wang, Na Xie, Chao-Fan Xu, Lu-Xiong Zhang, Fuquan Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuqing Fuzhou CityFujian350300 China Fuqing Branch of Fujian Normal University Electronic Information and Engineering Institute Fuqing Fuzhou CityFujian350300 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030037475

In the research of virtual hepatic surgery, the synthesis of hepatic texture space and the texture mapping of the hepatic body model are inseparable. Different methods for hepatic volume texture on the authenticity and mapping speed tend to have much difference. Based on computer unified device architecture (CUDA), this paper tries to present a texture synthesis and mapping method for the accelerating of hepatic body, which is used to solve the problem that traditional method under the hepatic volumetric texture synthesis and mapping process cost too much time. This new method may eliminate the traditional serial correlation of texture synthesis. What’s more, it may improve the efficiency of texture synthesis when the multithreading parallel processing for synthesis in the hepatic volumetric texture space in selecting and distributing blocks based on CUDA architecture. © Springer Nature Switzerland AG 2019.

关键词： Mapping

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation

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Computers in Biology and Medicine 2025年 194卷 110527页

作者： Xuesheng Bian Jia Liu Sen Xu Weiquan Liu Leyi Mei Chaoshen Xiao Fan Yang School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China College of Computer Engineering Jimei University Yinjiang Road No. 183 Xiamen 361005 Fujian China National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility) Key Laboratory of Genetic Evolution & Animal Models and National Resource Center for Nonhuman Primates Kunming Institute of Zoology Chinese Academy of Sciences East Jiaochang Road No. 32 Kunming 650221 Yunnan China National Key Laboratory of Radar Signal Processing Xidian university No. 2 South Taibai Road Xian 710071 Shaanxi China School of Resources and Environmental Engineering Wuhan University of Science and Technology 947 Heping Avenue Qingshan District Xian 430081 Wuhan China

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

关键词： Attention mechanism Breast lesion segmentation Deep learning Feature fusion Ultrasound images

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The Reconfigurable Pipelined Variable-point FFT Processor Design

The Reconfigurable Pipelined Variable-point FFT Processor De...

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

作者： Jiale Wang Yizhuang Xie Bingyi Li Chen Yang Shankang Hu 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 China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

In the multi-mode SAR imaging processing targeted in this paper, the FFT requirement granularity ranges from 1024 to 32768. Therefore, this paper mainly describes the method of implementing reconfigurable and variable-level FFT units using radix-2 k , By analyzing and comparing the radix 2 k algorithm, the radix-2 3 is selected as the basis of the single-channel FFT butterfly, and the range of the point representation is extended by the butterfly transformation downward compatible with the radix-2 2 /radix-2. This paper realizes an architecture of the single channel delay feedback variable-point, high precision FFT processor.

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Application of Kalman Filter in Deformation Data processing of Ground-based Differential Interferometric radar

Application of Kalman Filter in Deformation Data Processing ...

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

作者： Aijun Tan Zefu Tan Weiming Tian Manjun Yan Ying Long Key Laboratory of Information and Signal Processing Chongqing Three Gorges University Chongqing China Key Laboratory of Electronic and Information Technology in Satellite Navigation(Beijing Institute of Technology) Ministry of Education Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Ground-based differential interferometric radar is widely used in deformation monitoring research, but the one-dimensional deformation information dynamically acquired along the radar line of sight is often affected by random noise error and measurement error. In order to solve this problem, this paper proposes a deformation data processing method of ground-based differential interferometric radar based on Kalman filter. Firstly, the principle of ground-based differential interference technology is deeply studied. Then, based on the characteristics of deformation data monitored by ground-based radar, the Kalman filter data processing model is used to filter and predict the data. Finally, the measured data of slope monitoring in Malanzhuang open-pit mining is used. The effectiveness of the algorithm is verified by simulation and the accuracy of deformation data processing is greatly improved.

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Aircraft detection in Remote Sensing image for Space-borne platform

Aircraft detection in Remote Sensing image for Space-borne p...

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

作者： Wei Tang Baozhao Jun Linbo Tang Yu Pan Dongling Jin Zhengpiao Quan Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Aircraft detection in optical remote sensing image is an important research direction in the field of remote sensing. The existing detection methods are difficult to achieve satisfactory results. Traditional detection methods are low robustness, due to manual feature modeling are difficult and subject to background interference; The deep learning target detection method, which improves the detection performance at the cost of complexity improvement, cannot be widely used in space-borne platforms limited resources. In view of the above problems, this paper proposes an aircraft target deep learning detection method of lightweight and multi-scale features. On the basis of the multi-scale target detection framework (SSD), the method firstly uses the dense connection structure and the double convolution channels to form the basic backbone networks with feature reuse and high computational efficiency. To improve the detection performance of the small aircraft target, the basis backbone network connects a residual module and deconvolution to compose the multi-scale feature fusion detection module. Compared with the current classical deep learning object detection methods, the experimental results show that the proposed method has the advantages of maintaining low computational complexity and achieving high detection accuracy.

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Adaptive Mainlobe Interference Suppression in Sparse Distributed Array radar Based on Synthetic Wideband signal 2nd

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2nd International Conference on Machine Learning and intelligent Communications, MLICOM 2017

作者： Luo, Jian Zhang, Honggang Song, Yuanyuan Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783319734460

This paper proposes a mainlobe interference suppression method in distributed array radar (DAR) based on stepped frequency synthetic wideband signal. Due to the equivalent large aperture of DAR, it is possible to cancel the mainlobe interference without target signal suppression. The applying of stepped frequency synthetic wideband signal can avoid the different time delays arriving at each array compared with the traditional instantaneous wideband Chirp (linear frequency modulation) signal. Moreover, it can reduce the computational complexity. This method employs the narrowband adaptive processing to each sub-pulse of the stepped frequency signal, and then synthesizes the high resolution range profile (HRRP). As a result, mainlobe interference suppression under wideband signal is accomplished. Mainlobe interference suppression experiment is carried out by using an S-band experimental radar system, an S-band noise jammer and a target simulator. The measured data is processed employing the proposed method, and the result verifies the effectiveness of this algorithm. © 2018, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Beamforming

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Automated Selection of Evolutionary Multi-objective Optimization Algorithms

Automated Selection of Evolutionary Multi-objective Optimiza...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ye Tian Shichen Peng Tobias Rodemann Xingyi Zhang Yaochu Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Honda Research Institute Europe Offenbach/Main Germany Department of Computer Science University of Surrey Guildford Surrey United Kingdom

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In the last two decades, many evolutionary algorithms have shown promising performance in solving a variety of multi-objective optimization problems (MOPs). Since there does not exist an evolutionary algorithm having the best performance on all the MOPs, it is unreasonable to use a single evolutionary algorithm to tackle all the MOPs. Since many real-world MOPs are computationally expensive, selecting the best evolutionary algorithm from multiple candidates via empirical comparisons is also impractical. To address the above issues, this paper proposes an automated algorithm selection method for choosing the most suitable evolutionary algorithm for a given MOP. The proposed method establishes a predictor based on the performance of a set of candidate evolutionary algorithms on multiple benchmark MOPs, where the inputs of the predictor are the explicit and implicit features of an MOP, and the output is the index of the evolutionary algorithm having the best performance on the MOP. Experimental results indicate that the evolutionary algorithm suggested by the proposed method is highly competitive among all the candidate evolutionary algorithms, demonstrating the practical value of the proposed method for engineers to select an evolutionary algorithm for their applications.

关键词： Evolutionary computation Benchmark testing Feature extraction Optimization Prediction algorithms Convergence Linear programming

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Spaceborne Bistatic InSAR Geolocation with Geostationary Transmitter

Spaceborne Bistatic InSAR Geolocation with Geostationary Tra...

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

作者： Yuekun Wang Zheng Lu Feng Tian Zhiyong Suo Zhenfang Li Yu Zhu Nanjing Research Institute of Electronics Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The bistatic interferometric synthetic aperture radar (InSAR) system, with a geosynchronous earth orbit (GEO) illuminator and two low-earth-orbit (LEO) receivers, has the advantages of flexibility, lightweight receiving platform and low cost. However, its geometry of bistatic interferometry is complex, and its slant range equation is in the form of round-trip delay. The traditional three-dimensional (3D) positioning methods of InSAR with `one-transmitter and two-receivers' and repeat tracks are invalid. In order to solve this problem, firstly, we establish the bistatic InSAR target positioning equations. Secondly, according to the interferometric geometry, the conversion relationship between baseline vector and slant range vector is constructed. Then, we derive and present a closed-form solution of the bistatic InSAR target positioning in the form of geostationary GEO SAR orbit. Finally, the effectiveness of the proposed method is verified by simulation data.

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