Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-30T19:34:47.309Z Has data issue: false hasContentIssue false
  • English
  • Français

Virtual global positioning system construction approach for unmanned surface vessel based on Dempster–Shafer theory and broad learning framework

Published online by Cambridge University Press:  16 December 2022

Chuang Zhang ,
Chunyan Cao ,
Kaihang Kang ,
Chen Guo  and
Muzhuang Guo
Chuang Zhang*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Chunyan Cao
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Kaihang Kang
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Chen Guo
Affiliation:
College of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, China
Muzhuang Guo
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
*
*Corresponding author. E-mail: zhchuangdmu@163.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Integrated navigation systems made up of a strap-down inertial navigation system (SINS) and global positioning system (GPS) are increasingly being used to improve the position, speed, and attitude information of unmanned surface vessels (USV). However, a GPS outage could occur due to the dependence of GPS performance on the external environment and the number of available satellites. This study uses an innovative combination of Dempster–Shafer (DS) theory and broad learning (BL) method to design a SINS/GPS integrated navigation system. First, the velocity and position information derived from the SINS and their corresponding GPS were fused using DS fusion rules, while the SINS error was modelled using the BL method. A ‘virtual’ GPS was then designed using the proposed DS–BL approach to provide the speed and position information when the GPS signal was interrupted, thereby ensuring the continuous navigation of the USV. The results of both simulation and sea trial demonstrate that the proposed virtual GPS estimation approach is effective, and the navigational accuracy of the proposed method is superior to other methods.

Keywords

unmanned surface vesselstrap-down inertial navigation systemglobal positioning systemDempster–Shaferbroad learning
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1144 - 1166
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdel-Hafez, M. F., Saad Edd In, K. and Jarrah, M. A. (2015). Constrained low-cost GPS/INS filter with encoder bias estimation for ground vehicles applications. Mechanical Systems & Signal Processing, 58-59, 285–297.CrossRefGoogle Scholar
Aggarwal, P., Bhatt, D., Devabhaktuni, V. and Bhattacharya, P. (2013). Dempster Shafer neural network algorithm for land vehicle navigation application. Information Sciences, 253, 26–33.CrossRefGoogle Scholar
Aslinezhad, M., Malekijavan, A. and Abbasi, P. (2020). ANN-assisted robust GPS/INS information fusion to bridge GPS outage. EURASIP Journal on Wireless Communications and Networking, 2020(1), 129.CrossRefGoogle Scholar
Bai, M., Huang, Y., Zhang, Y. and Jia, G. (2020). A novel progressive Gaussian approximate filter for tightly coupled GNSS/INS integration. IEEE Transactions on Instrumentation and Measurement, 69(6), 3493–3505.CrossRefGoogle Scholar
Bai, M., Huang, Y., Chen, B. and Zhang, Y. (2021a). A Novel Robust Kalman Filtering Framework Based on Normal-Skew Mixture Distribution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–17.Google Scholar
Bai, M., Huang, Y., Zhang, Y. and Chen, F. (2021b). A novel heavy-tailed mixture distribution based Robust Kalman filter for cooperative localization. IEEE Transactions on Industrial Informatics, 17(5), 3671–3681.CrossRefGoogle Scholar
Bhatt, D., Babu, S. R. and Chudgar, H. S. (2016). A novel approach towards utilizing Dempster Shafer fusion theory to enhance wifi positioning system accuracy. Pervasive and Mobile Computing, 37, 115–123.10.1016/j.pmcj.2016.06.014CrossRefGoogle Scholar
Chen, X., Xu, Y., Li, Q., Tan, J. and Shen, C. (2016). Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM. Measurement, 92, 243–251.CrossRefGoogle Scholar
Chen, Q., Yin, C., Zhou, J., Wang, Y., Wang, X. and Chen, C. (2018). Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks. IEEE Sensors Journal, 18(11), 4561–4569.CrossRefGoogle Scholar
Chen, K., Chang, G. and Chen, C. (2021). GINav: A MATLAB-based software for the data processing and analysis of a GNSS/INS integrated navigation system. GPS Solutions, 25, 108.CrossRefGoogle Scholar
Chiang, K. W. and Huang, Y. W. (2008). An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Applied Soft Computing, 8(1), 722–733.CrossRefGoogle Scholar
Doostdar, P., Keighobadi, J. and Hamed, M. A. (2019). INS/GNSS integration using recurrent fuzzy wavelet neural networks. GPS Solutions, 24(1), 29.CrossRefGoogle Scholar
El-Sheimy, N., Chiang, K. W. and Noureldin, A. (2006). The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments. IEEE Transactions on Instrumentation & Measurement, 55(5), 1606–1615.CrossRefGoogle Scholar
Fang, J. C., Yin, R. and Lei, X. S. (2015). An adaptive decoupling control for three-axis gyro stabilized platform based on neural networks. Mechatronics, 27, 38–46.CrossRefGoogle Scholar
Georgy, J., Karamat, T., Iqbal, U. and Noureldin, A. (2011). Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter. GPS Solutions, 15(3), 239–252.CrossRefGoogle Scholar
Han, H., Wang, J. and Mingyi, D. U. (2018). GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update. Chinese Journal of Aeronautics, 31(03), 142–152.CrossRefGoogle Scholar
He, H., Luo, D., Lee, W. J., Zhang, Z., Cao, Y. and Lu, T. (2018). A contactless insulator contamination levels detecting method based on infrared images features and RBFNN. IEEE Transactions on Industry Applications, 55(3), 2455–2463.CrossRefGoogle Scholar
Jain, S. K. and Singh, S. N. (2013). Low-order dominant harmonic estimation using adaptive wavelet neural network. IEEE Transactions on Industrial Electronics, 61(1), 428–435.CrossRefGoogle Scholar
Li, Z., Chang, G., Gao, J., Jian, W. and Hernandez, A. (2016). GPS/UWB/MEMS-IMU tightly coupled navigation with improved robust Kalman filter. Advances in Space Research, 58(11), 2424–2434.CrossRefGoogle Scholar
Li, J., Song, N., Yang, G., Li, M. and Cai, Q. (2017). Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm. Information Fusion, 35(C), 1–10.CrossRefGoogle Scholar
Lin, Z., Qiu, H. and Feng, Y. (2015). Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system. Measurement, 80, 138–147.Google Scholar
Liu, Y., Fan, X., Chen, L., Jian, W., Liang, L. and Ding, D. (2017). An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles. Mechanical Systems and Signal Processing, 100, 605–616.CrossRefGoogle Scholar
Liu, Y., Pal, N. R., Marathe, A. R. and Lin, C. (2018). Weighted fuzzy Dempster-Shafer framework for multimodal information integration. IEEE Transactions on Fuzzy Systems, 26(1), 338–352.CrossRefGoogle Scholar
Mu, X., He, B., Wu, S., Zhang, X. and Yan, T. (2020). A practical INS/GPS/DVL/PS integrated navigation algorithm and its application on autonomous underwater vehicle. Applied Ocean Research, 106(3), 102441.CrossRefGoogle Scholar
Ng, W. (2016). MLPNN training via a multiobjective optimization of training error and stochastic sensitivity. IEEE Transactions on Neural Networks & Learning Systems, 27(5), 978–992.Google Scholar
Noureldin, A., El-Shafie, A. and Bayoumi, M. (2011). GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Information Fusion, 12, 48–57.CrossRefGoogle Scholar
Petturiti, D. and Vantaggi, B. (2017). Upper and lower conditional probabilities induced by a multivalued mapping. Journal of Mathematical Analysis and Applications, 458(2), 1214–1235.CrossRefGoogle Scholar
Sharaf, R., Tarbouchi, M., El-Shafie, A. and Noureldin, A. (2005) Real-time Implementation of INS/GPS Data Fusion Utilizing Adaptive Neuro-Fuzzy Inference System. Proceedings of the National Technical Meeting of the Institute of Navigation. San Diego, CA, 235–242.Google Scholar
Shen, C., Zhang, Y., Tang, J., Cao, H. and Liu, J. (2019). Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks. Mechanical Systems and Signal Processing, 133, 106222.CrossRefGoogle Scholar
Specht, M., Stateczny, A., Specht, C., Widźgowski, S., Lewicka, O. and Wiśniewska, M. (2021). Concept of an innovative autonomous unmanned system for bathymetric monitoring of shallow waterbodies (INNOBAT system). Energies, 14, 5370.CrossRefGoogle Scholar
Stateczny, A., Specht, C., Specht, M., Brčić, D., Jugović, A., Widźgowski, S., Wiśniewska, M. and Lewicka, O. (2021). Study on the positioning accuracy of GNSS/INS systems supported by DGPS and RTK receivers for hydrographic surveys. Energies, 14, 7413.CrossRefGoogle Scholar
Tan, X., Wang, J. and Zhao, C. (2015). Neural network aided adaptive UKF algorithm for GPS/INS integration navigation. Acta Geodaetica et Cartographica Sinica, 44(4), 384–391.Google Scholar
Tao, Z. and Xu, X. (2012). A new method of seamless land navigation for GNSS/INS integrated system. Measurement, 45(4), 691–701.Google Scholar
Vukovi, N., Petrovi, M. and Miljkovi, Z. (2018). A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression. Applied Soft Computing, 70, 1083–1096.CrossRefGoogle Scholar
Wang, Q., Cui, X., Li, Y. and Ye, F. (2017). Performance enhancement of a USV INS/CNS/DVL integration navigation system based on an adaptive information sharing factor federated filter. Sensors, 17, 239.CrossRefGoogle Scholar
Wang, D., Xu, X., Yao, Y. and Zhang, T. (2021). Virtual DVL reconstruction method for an integrated navigation system based on DS-LSSVM algorithm. IEEE Transactions on Instrumentation and Measurement, 70, 8501913.Google Scholar
Xu, L. and Xu, Q. (2017). A reliable fusion positioning strategy for land vehicles in GPS-denied environments based on low-cost sensors. IEEE Transactions on Industrial Electronics, 64(4), 3205–3215.Google Scholar
Xu, Z., Li, R., Rizos, R. and Xu, R. (2010). Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration. Journal of Navigation, 63(2), 289–300.CrossRefGoogle Scholar
Zhang, Y. (2019). A fusion methodology to bridge GPS outages for INS/GPS integrated navigation system. IEEE Access, 7, 61296–61306.CrossRefGoogle Scholar
Zhang, C., Guo, C. and Zhang, D. (2018). Ship navigation via GPS/IMU/LOG integration using adaptive fission particle filter. Ocean Engineering, 156, 435–445.CrossRefGoogle Scholar
Zhou, C., Lu, X. and Huang, M. H. (2016). Dempster-Shafer theory-based robust least squares support vector machine for stochastic modelling. Neurocomputing, 182, 145–153.CrossRefGoogle Scholar
Zhu, F., Shen, Y., Wang, Y., Jia, J. and Zhang, X. (2021a). Fusing GNSS/INS/vision with a priori feature map for high-precision and continuous navigation. IEEE Sensors Journal, 21, 23370–23381.CrossRefGoogle Scholar
Zhu, G., Ma, Y., Li, Z., Malekian, R. and Sotelon, M. (2021b). Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs with Input Saturation. IEEE Transactions on Intelligent Transportation Systems, 1–13.Google Scholar
Zou, L., Yu, X., Li, M., Lei, M. and Yu, H. (2020). Nondestructive identification of coal and gangue via near-infrared spectroscopy based on improved broad learning. IEEE Transactions on Instrumentation and Measurement, 69(10), 8043–8052.Google Scholar