针对卫导受限场景中，导航系统精度难以满足要求的问题，提出一种惯导/数据链/卫导紧组合算法，该算法采用卫导接收机和数据链定位解算之前的伪距信息和无线测距信息与惯导信息融合对惯导误差进行修正，只要存在卫导可见星或者数据链源节点即可进行信息融合，最大程度上利用场景中的导航观测量，改善了导航信息品质。试验结果表明，在数据链通信良好的情况下，增加有限的卫导观测量能够改善导航系统精度；在数据链弱联通的情况下，通过有限卫导观测量的引入使得导航系统能够持续输出高精度的导航信息。
In order to meet the accuracy requirements of navigation systems under GNSS restricted conditions, an inertial navigation system(INS)/data link(DL)/ Global Navigation Satellite System(GNSS) tightly coupled integrated algorithm is proposed. This algorithm corrects the INS error by fusing the pseudo range information of GNSS receiver and wireless ranging information of DL positioning with INS information. As long as there are GNSS visible satellites or DL source nodes, information fusion can be carried out, maximizing the use of navigation observations in the scene and improving the quality of navigation information. The experimental results indicated that adding limited satellite observation measurements could improve the accuracy of the navigation system under good data link communication. In the case of weak data link connectivity, the introduction of limited satellite observation measurements enabled the navigation system to continuously output high-precision navigation information.
2024,46(13): 141-145 收稿日期：2023-09-28
DOI：10.3404/j.issn.1672-7649.2024.13.025
分类号：TN965
基金项目：中船集团预研项目（626010306）
作者简介：傅金琳(1984-),女,研究员,研究方向为多源导航信息融合算法
参考文献：
[1] LIU Y, LI S, FU Q, et al. Impart assessment of GNSS spoofing attacks on INS/GNSS integrated navigation system[J]. Sensors, 2018, 18(5): 1433-1453.
[2] WANG M K, YU P D, LIY Z. Performance analysisof GNSS/INS loosely coupled integration systems under GNSS signal blocking environment[J]. E3S Web of Conferences, 2020, 18(12): 4108-4123.
[3] 袁洪, 张扬, 来奇峰, 等. 低轨星座/惯导紧组合导航技术研究[J]. 导航定位与授时, 2022, 9(1): 41-49.
YUAN Hong, ZHANG Yang, LAI Qifeng，etal. Research of LEO constellation/INS tight integrated navigation[J]. Navigation Positioning and Timing, 2022, 9(1): 41-49.
[4] 张鑫, 冯双记. 机载INS/GNSS深组合导航系统发展现状[J]. 光学与光电技术, 2021, 19(6): 88-96.
ZHANG Xin, FENG Shuang-ji. Development status of airborne INS/GNSS deep integrated navigation system[J]. Optics and Optoelectronic Technology, 2021, 19(6): 88-96.
[5] GIANLUCA F, MARCO P, GIANLUCA M. Loose and tight GNSS/INS integrations: comparison of performance assessed in real urban scenarios[J]. Sensors, 2017, 17(2): 27-29.
[6] BAN Y L, NIU X J, ZHANG T S, et al. Modeling and quantitative analysis of GNSS/INS deep integration tracking loops in high dynamics[J]. Micromachines 2017, 8(9), 272-292.
[7] CRESPILLO O G, MEDINA D, SKALOUD J, et al. Tightly coupled GNSS/INS integration based on robust M-estimators[J]. 2018 IEEE/ION Position, Location and Navigation Symposium(PLANS), IEEE, 2018: 1554-1561.
[8] 王富, 韩保民, 胡亮亮, 等. 城市复杂环境下GNSS/INS 组合导航算法研究[J]. 大地测量与地球动力学, 2022, 42(1): 15-20.
WANG Fu, HAN Bao min, HU Liang lian,etal. Research on CNSS/INS integrated navigation algorithm bin complex urban environments[J]. Journal of Geordesy and Geodymics, 2022, 42(1): 15-20.
[9] 周彬, 赵万良, 荣义杰, 等. 基于MEMS-INS/GNSS组合导航抗差自适应Kalman滤波算法[J]. 导航与控制, 2018, 17(4): 14-20 .
ZHOU Bin, ZHAO Wanliang, RONG Yijie, et al. Robust adaptive Kalman filter algorithm based on MEMS-INS/GNSS integrated navigation[J]. Navigation and Control, 2018, 17(4): 14-20 .
[10] 葛志敏, 江金光, 张超, 等. 改进抗差自适应EKF算法在GNSS/INS组合导航中的应用[J]. 大地测量与地球动力学, 2023, 43(7): 740-744
GE Zhimin, JIANG Jinguang, ZHANG Chao,etal. Application of improved robust and adaptive EKF algorithm in GNSS/INS integrated navigation[J]. Journal of Geodesyand Geodynamics, 2023, 43(7): 740-744.
[11] 赵方, 吴凡. GNSS/INS自适应智能组合导航算法[J]. 北京邮电大学学报, 2022, 45(2): 1-8
ZHAO Fang,WU Fan. Adaptive Integrated Navigation Based Artificial Intelligence[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 1-8
[12] CUI B B, CHEN X, XU Y, et al. Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS[J]. ISA Transactions., 2017, 66(10): 460-468.
[13] LI Zengke. WiFi/PDR integrated navigation with robustly constrained kalman filter[J]. Measurement Science and Technology, 2020, 31(8): 526-536.
[14] LI Binghao, ZHAO Kai, EDUARDO B. A UWB-based indoor positioning system employing neural networks[J]. Journal of Geovisualization and Spatial Analysis, 2020, 4(2): 707-713.
[15] GAO Fanqi. An improved INS/PDR/UWB integrated positioning method for indoor foot-mounted pedestrians[J]. Sensor Review, 2019, 39(3): 318-331.
[16] 杨秀梓, 王敬东, 刘亚飞, 等. UWB/惯性技术组合优化的室内定位技术研究[J]. 电子测量技术, 2019, 42(15): 132-138.
YANG Xiuzi, WANG Jingdong, LIU Yafei, et al. Research on indoor positioning technology based on UWB/inertial technology combination optimization[J]. Electronic Measurement Technology, 2019, 42(15): 132-138.
