Decentralized extend information filter for cooperative localization of UUVs

doi:10.11947/j.AGCS.2022.20210144

Abstract

Abstract: Compared with the single UUV, the cooperative work of unmanned underwater vehicle (UUV) can expand the sensing range and accomplish complex tasks.Due to the complexity of the underwater environment and the limitation of UUV sensors, the huge real-time communication required by the traditional Kalman filtering method is difficult to realize in the underwater environment,which makes the result of current cooperative localization is not rigorous. To solve the problem, a new decentralized extend information filter method for UUVs cooperative localization is proposed. Based on the rigorous theory, the distributed cooperative localization of UUVs is realized.Each UUV establishes its own state chain according to the local sensor data, broadcasts its observation information, and cooperates to complete the Cholesky correction. The consistency between the proposed decentralized filter and centralized filter is proved, and the comparison experiment between the new method and the traditional method is carried out.Theoretical simulation analysis shows that,compared with the traditional methods that the observation updating of the single UUV or the two UUVs will result in the observation updating of the whole UUVs, the new method realizes the observation updating only related to the UUVs that directly involved in the observation.The proposed method reduces the communication payloads and has good expansibility for observation information.

Key words: UUV, cooperative location, extent information filter, Decentralized, Cholesky

CLC Number:

P228

DU Zhenqiang, CHAI Hongzhou, XIANG Minzhi, ZHANG Fan, HUANG Ziru, ZHU Huawei. Decentralized extend information filter for cooperative localization of UUVs[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 182-191.

References

[1] LEE P M, JUN B H, LIM Y K. Review on underwater navigation system based on range measurements from one reference[C]//Proceedings of OCEANS 2008-MTS/IEEE Kobe Techno-Ocean. Kobe, Japan:IEEE, 2008:1-5.
[2] PAULL L, HUANG G, SETO M, et al. Communication-constrained multi-AUV cooperative SLAM[C]//Proceedings of 2015 IEEE international conference on robotics and automation (ICRA). Seattle, WA, USA:IEEE, 2015:509-516.
[3] YANG Yuanxi, XU Tianhe, XUE Shuqiang. Progresses and prospects of marine geodetic datum and marine navigation in China[J]. Journal of Geodesy and Geoinformation Science, 2018, 1(1):16-24.
[4] WANG J, XU T, ZHANG B, et al. Underwater acoustic positioning based on the robust zero-difference Kalman filter[J]. Journal of Marine Science and Technology, 2020(7):1-16.
[5] KALWA J, PASCOAL A, PERRIER M, et al. Coordination and control of cooperating heterogeneous unmanned systems in uncertain environments[J]. Annex 1, EC Project IST, 2006:35223.
[6] SCHOFIELD O, CHANT R, KOHUT J, et al. The growth of the New Jersey shelf observing system for monitoring plumes and blooms on the mid-atlantic continental shelf[C]//Proceedings of Oceans'04 MTS/IEEE Techno-Ocean'04(IEEE Cat. No. 04CH37600). Kobe, Japan::IEEE, 2004(1):127-132.
[7] 翟国君, 莫谟涛. 海洋测绘的现状与发展[J]. 测绘通报, 2001(6):7-9. ZHAI Guojun, HUANG Motao, OUYANG Yongzhong, et al. The present and future of hydrographic surveying and charting[J]. Bulletin of Surveying and Mapping, 2001(6):7-9.
[8] 杨元喜, 徐天河, 薛树强. 我国海洋大地测量基准与海洋导航技术研究进展与展望[J]. 测绘学报, 2017, 46(1):1-8. DOI:10.11947/j.AGCS/2017.20160519. YANG Yuanxi, XU Tianhe, XUE Shuqiang. Progresses and Prospects in Developing Marine Geodetic Datum and Marine Navigation of China[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1):1-8. DOI:10.11947/j.AGCS/2017.20160519.
[9] 赵建虎, 欧阳永忠, 王爱学. 海底地形测量技术现状及发展趋势[J]. 测绘学报, 2017, 46(10):1786-1794. DOI:10.11947/j.AGCS/2017.20170276. ZHAO Jianhu, OUYANG Yongzhong, WANG Aixue. Status and development tendency for seafloor terrain measurement technology[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1786-1794. DOI:10.11947/j.AGCS/2017.20170276.
[10] TAN Y T, GAO R, CHITRE M. Cooperative path planning for range-only localization using a single moving beacon[J]. IEEE Journal of Oceanic Engineering, 2014, 39(2):371-385.
[11] HUANG Y, ZHANG Y, XU B, et al. A new adaptive extended Kalman filter for cooperative localization[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 54(1):353-368.
[12] VAGANAY J, LEONARD J J, CURCIO J A, et al. Experimental validation of the moving long base-line navigation concept[C]//Proceedings of 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No. 04CH37578). Sebasco, ME, USA:IEEE, 2004:59-65.
[13] BAHR A, LEONARD J J, FALLON M F. Cooperative localization for autonomous underwater vehicles[J]. The International Journal of Robotics Research, 2009, 28(6):714-728.
[14] LIU M, SHEN J, ZHANG J, et al. A cooperative localization method of UUV based on unscented Kalman filter[J]. Torpedo Technology, 2011, 19(3):205-208.
[15] DONG P, CHENG J, LIU L, et al. INS/USBL nonlinear integrated navigation method based on relative information observation[J]. Systems Engineering and Electronic Technology, 2019, 41:402-408.
[16] LI Q, NAQVI S M, NEASHAM J, et al. Robust cooperative navigation for AUVs using the student's T distribution[C]//Proceedings of 2017 Sensor Signal Processing for Defence Conference (SSPD). Looidon, UK:IEEE, 2017:1-5.
[17] 杨元喜. 自适应动态导航定位[M]. 北京:测绘出版社, 2006. YANG Yuanxi. Adaptive dynamic navigation and positioning[M]. Beijing:Surveying and Mapping Press, 2006.
[18] 高为广, 杨元喜, 张双成. 基于当前加速度模型的抗差自适应Kalman滤波[J].测绘学报,2006, 35(1):15-18. GAO Weiguang, YANG Yuanxi, ZHANG Shuangcheng. Adaptive robust Kalman filtering based on the current statistical model[J]. Acta Geodaetica et Cartographica Sinica,2006, 35(1):15-18.
[19] SUN C, ZHANG Y, WANG G, et al. A maximum correntropy divided difference filter for cooperative localization[J]. IEEE Access, 2018(6):41720-41727.
[20] HARRIS Z J, WHITCOMB L L. Preliminary evaluation of cooperative navigation of underwater vehicles without a DVL utilizing a dynamic process model[C]//Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD, Australia:IEEE, 2018:4897-4904.
[21] 尹潇, 柴洪洲, 向民志, 等. 附加运动学约束的BDS抗差UKF导航算法[J]. 测绘学报, 2020, 49(11):1399-1406. YIN Xiao, CHAI Hongzhou, XIANG Minzhi, et al. Robust UKF algorithm with motion constraint in BDS navigation[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(11):1399-1406.
[22] ZHAO S, WANG Z, HE K, et al. Investigation on underwater positioning stochastic model based on acoustic ray incidence angle[J]. Applied Ocean Research, 2018, 77:69-77.
[23] WANG Z, ZHAO S, JI S, et al. Real-time stochastic model for precise underwater positioning[J]. Applied Acoustics, 2019(150):36-43.
[24] XU B, RAZZAQI A A, YALONG L. Cooperative localisation of AUVs based on huber-based robust algorithm and adaptive noise estimation[J]. The Journal of Navigation, 2019, 72(4):875-893.
[25] MU H, BAILEY T, THOMPSON P, et al. Decentralised solutions to the cooperative multi-platform navigation problem[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2):1433-1449.