电子科学技术研究院
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| 导师代码： |
20321
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| 导师姓名： |
郭承军
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| 性 别： |
男 |
| 特 称： |
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| 职 称： |
副研究员
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| 学 位： |
工学博士学位
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| 属 性： |
专职 |
| 电子邮件： |
johnsonguo@uestc.edu.cn
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| 学术经历：
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| 个人简介：
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院士秘书，国际大地测量协会GNSS干扰与欺骗工作组成员，中国卫星导航年会分会主席。
长期致力于时空体系与多源融合导航，智能非合作信号处理以及灵巧（自决策、自适应、自作业）整机（多功能、多性能、多组合）架构设计等方向的设计、规划和研究工作。在建立建筑智能态势感知理论与方法、解决动态环境多源融合导航关键技术难题、构建灵巧整机系统全域全时感知体系等方面做出相应的贡献。
主要研究方向：1. 智能态势感知；2. 导航与集群智能；3.灵巧整机架构设计。
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| 科研项目：
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[1] 基于多传感器融合的电动轮雨雾补盲智能车辅系统研究与示范应用；
[2] 空地协同区域导航增强系统智能终端软件采集显示系统；
[3] 现代化导航信号服务质量异常变化感知与评估技术；
[4] 基于XXX一体化设计技术研究；
[5] GNSS/INS欺骗式干扰技术研究;
[6] W波段高集成有源阵面技术研究；
[7] 异构D群有限信息交互协同DJ引导技术研究术;
[8] 基于生成DK网的异构时M目标信息补齐与重构技术研究；
[9] XX相控阵雷达XXX异形XXX快速标校测试技术；
[10] 星地通信海上多径干扰仿真算法;
[11] 轻量化多信息通信和北斗定位技术;
[12] 通信降级干扰对抗分析与人机混合协同决策控制技术;
[13] 海上电磁频谱评估与占用度预测；
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| 研究成果：
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2018年至今的主要文章：
[1]. Inversion of PM2.5 atmospheric refractivity profile based on AlexNet model from the perspective of , ,electromagnetic wave propagation Environmental Science and Pollution Research , 2020 : 1-14 (SCI IF:5.8)
[2]. Aida Darvishan; Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network Energy Conversion and Management, 2019, 183: 149-158. (SCI IF:10.04)
[3]. Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network, Neural Computing and Applications, 2019, (SCI IF:6.0)
[4]. Analysis and design of an , attitude calculation algorithm based on elman neural network for SINS, Cluster Computing 2018 (SCI IF:4.4)
[5]. Research on multi-constellation GNSS compatible acquisition strategy based on GPU high-performance operation, EURASIP Journal on Wireless Communications and Networking, 2018, 118. (SCI IF:2.455)
[6].End-to-End Powerline Detection Based on Images from UAVs, Remote Sensing, 2023, 15(6), 1570. (SCI IF:5.0)
[7]. Multi-sensor Fusion for Autonomous Deep Space Navigation[C]. ION GNSS 2020, 2020.
[8]. Identification, Attitude Estimation and Capture of Tumbling Spacecraft Based on SLAM[C]. ION GNSS 2020, 2020.
[9]. A fast fault recovery method for formation networks of micro-nano satellites based on maximum lifetime[C]. 2020:5.
[10]. Classification method of 5G satellite network computing tasks based on edge data offloading[C]. 2020:5.
[11]. A GNSS/INS integrated navigation method based on deep learning[C]. IAC 2020.
[12]. Enhanced multi-sensor data fusion method using recurrent neural network[C]. IAC 2020.
[13]. A Novel Self-Learning GNSS/INS Integrated Navigation Method[C].ION GNSS+ 2021.
[14]. GNSS Interference Signal Recognition Based on Deep Learning and Fusion Time-Frequency Features[C].ION GNSS+ 2021.
[15] A Modulation Recognition Method for OFDM based on Compressed Sensing[C]. IAC 2021.
[16] Application of Improved Targeted Detection algorithm in Aerial Remote Sensing Image. IAC 2021.
[17] Research on Video Moving Target Detection Algorithm based on Improved Target Detection Network. IAC 2021.
[18] Depth Image Optimization of Field Environment based on Fusion Filtering. IAC 2021.
[19] Satellite Video Target Tracking Based on Improved YOLOv3 and Deep Sort[C]. ION GNSS+ 2022.
[20] Remote Sensing Image Target Detection Based on Improved Faster R-CNN[C]. ION GNSS+ 2022.
[21] A Robust RF Fingerprint Extraction Scheme for GNSS Spoofing Detection[C]. ION GNSS+ 2023.
[22] An interference detection algorithm for GNSS frequency-hopping signals based on optimized energy and multi-segment spectral clustering[C]. ION GNSS+ 2023.
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