PNT intelligent services

doi:10.11947/j.AGCS.2021.20210051

Abstract

Abstract: The important research direction of positioning, navigation and timing (PNT) is PNT intelligent services or smart PNT services. The most significant function of the PNT intelligent services is to sense the environment and the user requirements, to realize the intelligent integration of PNT information, intelligent modification of observation model and the intelligent fusion of multi-PNT information, and then to deliver the PNT service accordingly. This paper describes and analyzes the key techniques of intelligent PNT in different aspects, including the intelligent sensing, intelligent model, intelligent data integration and intelligent service. Additionally, the criteria for the PNT intelligent services are proposed, that is, the "availability criterion" for the intelligent PNT information integration, the "reliability criterion" for the intelligent functional model optimization, the "uncertainty criterion" for the stochastic model intelligent modification, the "accuracy criterion" for the intelligent PNT data fusion, and the "efficiency criterion" for the smart PNT services, as well as the "continuity criterion" for high dynamic users. The study further indicates that the comprehensive (or integrated) PNT is the foundation of the resilient PNT, the resilient PNT is the foundation of the intelligent PNT, and the intelligent PNT is the key development direction of PNT services.

Key words: positioning, navigation and timing, intelligent sensing, intelligent model, intelligent fusion, intelligent services

CLC Number:

P228

YANG Yuanxi, YANG Cheng, REN Xia. PNT intelligent services[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8): 1006-1012.

References

[1] 杨元喜. 综合PNT体系及其关键技术[J]. 测绘学报, 2016, 45(5):505-510. DOI:10.11947/j.AGCS.2016.20160127. YANG Yuanxi. Concepts of comprehensive PNT and related key technologies[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(5):505-510. DOI:10.11947/j.AGCS.2016.20160127.
[2] YANG Yuanxi. Resilient PNT concept frame[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(3):1-7. DOI:10.11947/j.JGGS.2019.0301.
[3] 全耀. 浅谈人工智能的发展史[J]. 现代信息科技, 2019, 3(6):80-84. QUAN Yao. Talking about the development history of artificial intelligence[J]. Modern Information Technology, 2019, 3(6):80-84.
[4] 梁秀娟. 科学知识图谱研究综述[J]. 图书馆杂志, 2009, 28(6):58-62. LIANG Xiujuan. Review of mapping knowledge domains[J]. Library Journal, 2009, 28(6):58-62.
[5] 刘经南, 郭文飞, 郭迟, 等. 智能时代泛在测绘的再思考[J]. 测绘学报, 2020, 49(4):403-414. DOI:10.11947/j.AGCS.2020.20190539. LIU Jingnan, GUO Wenfei, GUO Chi, et al. Rethinking ubiquitous mapping in the intelligent age[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4):403-414. DOI:10.11947/j.AGCS.2020.20190539.
[6] 李德仁. 从测绘学到地球空间信息智能服务科学[J]. 测绘学报, 2017, 46(10):1207-1212. DOI:10.11947/j.AGCS.2017.20170263. LI Deren. From geomatics to geospatial intelligent service science[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1207-1212. DOI:10.11947/j.AGCS.2017.20170263.
[7] 龚健雅, 张翔, 向隆刚, 等. 智慧城市综合感知与智能决策的进展及应用[J]. 测绘学报, 2019, 48(12):1482-1497. DOI:10.11947/j.AGCS.2019.20190464. GONG Jianya, ZHANG Xiang, XIANG Longgang, et al. Progress and applications for integrated sensing and intelligent decision in smart city[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1482-1497. DOI:10.11947/j.AGCS.2019.20190464.
[8] 王密, 杨芳. 智能遥感卫星与遥感影像实时服务[J]. 测绘学报, 2019, 48(12):1586-1594. DOI:10.11947/j.AGCS.2019.20190454. WANG Mi, YANG Fang. Intelligent remote sensing satellite and remote sensing image real-time service[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1586-1594. DOI:10.11947/j.AGCS.2019.20190454.
[9] 杨必胜, 董震. 点云智能研究进展与趋势[J]. 测绘学报, 2019, 48(12):1575-1585. DOI:10.11947/j.AGCS.2019.20190465. YANG Bisheng, DONG Zhen. Progress and perspective of point cloud intelligence[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1575-1585. DOI:10.11947/j.AGCS.2019.20190465.
[10] CHITHAPURAM C, JEPPU Y, KUMAR C A. Artificial intelligence learning based on proportional navigation guidance[C]//Proceedings of 2013 International Conference on Advances in Computing, Communications and Informatics. Mysore:IEEE, 2013.
[11] BRASSAI S T, IANTOVICS B, ENǍCHESCU C. Artificial intelligence in the path planning optimization of mobile agent navigation[J]. Procedia Economics and Finance, 2012, 3:243-250.
[12] YANG Y, ZHANG S. Adaptive fitting of systematic errors in navigation[J]. Journal of Geodesy, 2005, 79:43-49.
[13] 杨元喜, 高为广. 基于方差分量估计的自适应融合导航[J]. 测绘学报, 2004, 33(1):22-26. YANG Yuanxi, GAO Weiguang. Integrated navigation by using variance component estimates of multi-sensor measurements and adaptive weights of dynamic model information[J]. Acta Geodaetica et Cartographica Sinica, 2004, 33(1):22-26.
[14] YANG Yuanxi, XU Tianhe. An adaptive Kalman filter based on sage windowing weights and variance components[J]. The Journal of Navigation, 2003, 56(2):231-240.
[15] YANG Yuanxi, GAO Weiguang. An optimal adaptive Kalman filter[J]. Journal of Geodesy, 2006, 80(4):177-183.
[16] YANG Y, HE H, XU G. Adaptively robust filtering for kinematic geodetic positioning[J]. Journal of Geodesy, 2001, 75(2-3):109-116.
[17] 尼克. 人工智能简史[M]. 北京:人民邮电出版社, 2017. NI Ke. A brief history of artificial intelligence[M]. Beijing:Posts&Telecom Press, 2017.
[18] WANG Zhen, ZHANG Jianwen, FENG Jianlin, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec City:AAAI, 2014:1112-1119.
[19] HOPFIELD J J. Neural networks and physical system with emergent collective computational abilities[J]. Proceedings of the National Academy of Sciences of the United States of America, 1982, 79(8):2554-2558.
[20] 高为广, 杨元喜, 张婷. 神经网络辅助的GPS/INS组合导航自适应滤波算法[J]. 测绘学报, 2007, 36(1):26-30. GAO Weiguang, YANG Yuanxi, ZHANG Ting. Neural network aided adaptive filtering for GPS/INS integrated navigation[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(1):26-30.
[21] 高为广, 杨元喜, 张婷. 一种提高神经网络泛化能力的自适应UKF滤波算法[J]. 武汉大学学报(信息科学版), 2008, 33(5):500-503. GAO Weiguang, YANG Yuanxi, ZHANG Ting. An adaptive UKF algorithms for improving the generalization of neural network[J]. Geomatics and Information Science of Wuhan University, 2008, 33(5):500-503.
[22] 高为广, 杨元喜. 神经网络辅助的GPS/INS组合导航故障检测算法[J]. 测绘学报, 2008, 37(4):403-409. GAO Weiguang, YANG Yuanxi. Neural network aided GPS/INS integrated navigation fault detection algorithms[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(4):403-409.
[23] GU Yang, CHEN Yiqiang, LIU Junfa, et al. Semi-supervised deep extreme learning machine for Wi-Fi based localization[J]. Neurocomputing, 2015, 166:282-293.
[24] HSU L T. GNSS multipath detection using a machine learning approach[C]//Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama:IEEE, 2017.
[25] SUN Rui, WANG Guanyu, ZHANG Wenyu, et al. A gradient boosting decision tree based GPS signal reception classification algorithm[J]. Applied Soft Computing, 2020, 86:105942.
[26] CHEN Guoliang, MENG Xiaolin, WANG Yunjia, et al. Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D Indoor Localization[J]. Sensors, 2015, 15(9):24595-24614.
[27] WANG Lei, GROVES P D, ZIEBART M K. GNSS shadow matching:improving urban positioning accuracy using a 3D city model with optimized visibility prediction scoring[C]//Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation. Nashville, 2012:423-437.
[28] 夏炎, 潘树国, 赵鹏飞, 等. 基于无监督学习的卫星NLOS信号检测方法[J]. 东南大学学报(自然科学版), 2019, 49(3):565-572. XIA Yan, PAN Shuguo, ZHAO Pengfei, et al. Unsupervised learning based satellite NLOS signal detection method[J]. Journal of Southeast University (Natural Science Edition), 2019, 49(3):565-572.
[29] HOPFIELD J J, TANK D W. "Neural" computation of decisions in optimization problems[J]. Biological Cybernetics, 1985, 52(3):141-152.
[30] 杨元喜, 刘焱雄, 孙大军, 等. 海底大地基准网建设及其关键技术[J]. 中国科学:地球科学, 2020, 50(7):936-945. YANG Yuanxi, LIU Yanxiong, SUN Dajun, et al. Seafloor geodetic network establishment and key technologies[J]. Science China Earth Sciences, 2020, 50(7):936-945.
[31] ZHANG Alan, ATIA M M. An efficient tuning framework for Kalman filter parameter optimization using design of experiments and genetic algorithms[J]. Navigation, 2020, 67(4):775-793.
[32] 杨元喜, 高为广. 基于多传感器观测信息抗差估计的自适应融合导航[J]. 武汉大学学报(信息科学版), 2004, 29(10):885-888. YANG Yuanxi, GAO Weiguang. Integrated navigation based on robust estimation outputs of multi-sensor measurements and adaptive weights of dynamic model information[J]. Geomatics and Information Science of Wuhan University, 2004, 29(10):885-888.
[33] YANG Yuanxi, CUI Xianqiang, GAO Weiguang. Adaptive integrated navigation for multi-sensor adjustment outputs[J]. The Journal of Navigation, 2004, 57(2):287-295.