Multi-source Integrated Navigation Algorithm for Iterated Maximum Posteriori Estimation Based on Sliding-window Factor Graph

XU Haowei;LIAN Baowang;LIU Shangbo

CN Submission Journal Articles Figures Search
Adv. Search

  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2019, Vol. 40 ›› Issue (4) : 807-819. DOI: 10.3969/j.issn.1000-1093.2019.04.016
Paper

Multi-source Integrated Navigation Algorithm for Iterated Maximum Posteriori Estimation Based on Sliding-window Factor Graph

  • XU Haowei1, LIAN Baowang1, LIU Shangbo1,2
Author information +
History +

Abstract

In the process of data fusion in multi-source integrated navigation using factor graph, the time-varying characteristics of the subsystem's observed noise have a great influence on the estimation accuracy of navigation state. In order to solve the problem, a Gaussian model-based method to estimate the mean vector and covariance matrix of sub-system observation is proposed. In the proposed method, the observed-measurement residuals for each iterative cycle in the process of factor graph optimization are utilized to update the maximum posteriori estimated values of mean vectors and covariance matrices. A more accurate estimated value of navigation state can be obtained by estimating the sub-system noise state. The influence of the new algorithm on the convergence of optimization process was also deduced. Both the simulated and experimental results show that, compared with the existing algorithms as factor graph, maximum likelihood estimation based factor graph and maximum posteriori based factor graph, the proposed factor graph method based on iterative maximum posteriori estimation can effectively improve the accuracy of navigation estimation when the subsystem observing state varies. Key

Key words

multi-sourceintegratednavigation / datafusion / factorgraph / inertialnavigationsystem / globalnavigationsatellitesystem / ultra-widebandnavigationsystem

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
XU Haowei, LIAN Baowang, LIU Shangbo. Multi-source Integrated Navigation Algorithm for Iterated Maximum Posteriori Estimation Based on Sliding-window Factor Graph. Acta Armamentarii. 2019, 40(4): 807-819 https://doi.org/10.3969/j.issn.1000-1093.2019.04.016

References



［1］LIZ, WANG R, GAO J. An approach to improve the positioning performance of GPS/INS/UWB integrated system with two-step filter[J]. Remote Sensing, 2017, 10(1): 19-34.
[2]WANGM, LI Y, FENG G, et al. Key technologies of GNSS/INS/VO deep integration for UGV navigation in urban canyon[C]∥Proceedings of the 11th Asian Control Conference. Gold Coast, Australia: IEEE, 2017: 2546-2551.
[3]XU H W, LIAN B W, TOTH C K, et al. An airborne LiDAR/INS integrated navigation algorithm based on fuzzy controlled SIFT[C]∥Proceedings of the 2017 International Technical Meeting of the Institute of Navigation. Monterey. CA, US: the Institute of Navigation, 2017: 313-326.
[4]YUAN G M, YUAN K F, ZHANG H W. A variable proportion adaptive federal Kalman filter for INS/ESGM/GPS/DVL integrated navigation system[C]∥Proceedings of the 4th International Joint Conference on Computational Sciences and Optimization. Kunming/Lijiang, Yunnan, China: IEEE, 2011: 978-981.
[5]钱进.改进的联合Kalman滤波及其在组合导航中的应用[J].火力与指挥控制,2009,34(7):142-144.
QIAN J. A modified federal Kalman filter algorithm and it's application at integrated navigation system[J]. Fire Control and Command Control, 2009, 34(7):142-144. (in Chinese)
[6]XU H W, LIAN B W. Fault detection for multi-source integrated navigation system using fully convolutional neural network[J]. IET Radar, Sonar & Navigation, 2018, 12(7):774-782.
[7]GAO W, ZHANG Y, WANG J. A strapdown inertial navigation system/Beidou/Doppler velocity log integrated navigation algorithm based on a cubature Kalman filter[J]. Sensors, 2014, 14(1): 1511-1527.
[8]何伟, 廉保旺, 冯晓明. 基于联邦卡尔曼滤波的组合导航定位算法[J]. 火力与指挥控制, 2012, 37(8):147-150.
HE W, LIAN B W, FENG X M. Integrated navigation and location algorithm based on Federated Kalman filter[J]. Fire Control and Command Control, 2012, 37(8):147-150. (in Chinese)
[9]高军强, 汤霞清, 张环,等. 基于因子图的车载INS/GNSS/OD组合导航算法[J]. 系统工程与电子技术, 2018, 40(11): 2547-2554.
GAO J Q, TANG X Q, ZHANG H, et al. Vehicle INS/GNSS/OD integrated navigation algorithm based on factor graph[J]. Systems Engineering and Electronics, 2018, 40(11): 2547-2554.(in Chinese)

[10]INDELMANV, WILLIAMS S, KAESS M, et al. Information fusion in navigation systems via factor graph based incremental smoothing[J]. Robotics and Autonomous Systems, 2013, 61(8):721-738.
[11]ZENG Q, CHEN W, LIU J, et al. An improved multi-sensor fusion navigation algorithm based on the factor graph[J]. Sensors, 2017, 17(3): 641-656.
[12]王慧哲, 曾庆化, 刘建业, 等. 基于因子图的无人机全源导航关键技术研究[J]. 导航与控制, 2017, 16(2):1-5.
WANG H Z, ZENG Q H, LIU J Y, et al. Research on the key technologyof UAV of all source position navigation based on factor graph[J].Navigation and Control, 2017, 16(2):1-5. (in Chinese)
[13]吕瑞, 陈龙, 翁雪, 等. 利用先验点图模型的SLAM后端优化算法[J]. 武汉大学学报(信息科学版), 2014, 39(6):745-749.
L R, CHEN L, WENG X. A back-end optimization algorithm of SLAM based on graph model with prior points[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):745-749.(in Chinese)
[14]CHIU H, WILLIAMS S, DELLAERT F, et al. Robust vision-aided navigation using sliding-window factor graphs[C]∥Proceedings of 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013: 46-53.
[15]CROUCH M S, MCGREGOR A, STUBBS D. Dynamic graphs in the sliding-window model[M]. London, UK: Springer, 2013.
[16]秦永元, 张洪钺，汪叔华. 卡尔曼滤波与组合导航原理[M]. 西安：西北工业大学出版社, 1998:277-281.
QIN Y Y, ZHANG H Y, WANG S H. Theory of Kalman filter and integrated navigation[M].Xi'an: Northwestern Polytechnic University Press, 1998:277-281.(in Chinese)
[17]INDELMAN V, WILLIAMS S, KAESS M. Factor graph based incremental smoothing in inertial navigation systems[C]∥Proceedings of the 15th International Conference on Information Fusion. Singapore: IEEE, 2012: 2154-2161.
[18]PAN J X, FANG K T. Growth curve models and statistical diagnostics[M]. New York, NY, US: Springer, 2002.
[19]ELISHA Y B, INDELMAN V. Active online visual-inertial navigation and sensor calibration via belief space planning and factor graph based incremental smoothing[C]∥Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE, 2017: 2616-2622.
[20]LIU H Y, ZHANG Z Y, GRIMM K J. Comparison of inverse Wishart and separation-strategy priors for Bayesian estimation of covariance parameter matrix in growth curve analysis[J]. Structural Equation Modeling: A Multidisciplinary Journal, 2016, 23(3):354-367.
[21]HARTLEY H O. The modified Gauss-Newton method for the fitting of non-linear regression functions by least squares[J]. Technometrics, 1961, 3(2): 269-280.
[22]ECKENHOFF K, GENEVA P, HUANG G. Direct visual-inertial navigation with analytical preintegration[C]∥Proceedings of IEEE International Conference on Robotics and Automation. Marina Bay Sands, Singapore: IEEE, 2017: 1429-1435.




第40卷
第4期2019年4月兵工学报ACTA
ARMAMENTARIIVol.40No.4Apr.2019

Accesses

Citation

Detail
Sections
Recommended

/

〈 〉