Enhancing the accuracy of position and speed parameter determination for carrying devices through artificial neural networks



  • Hoang Van Long Academy of Military Science and Technology
  • Tran Duc Thuan Academy of Military Science and Technology
  • Nguyen Quang Vinh Academy of Military Science and Technology
  • Nguyen Duc Anh (Corresponding Author) University of Fire Prevention and Fighting




GPS/INS integrated system; Artificial intelligence; GPS outages.


 This paper presents a method of applying an extended nonlinear Kalman filter to combine measured information from angular rate gyroscopes with magnetometers and accelerometers and satellite positioning information to estimate Rodrig - Hamilton parameters, position and speed of the carrier. In addition, the article also presents a method to improve the performance of the integrated global positioning system and inertial navigation system (GPS/INS) during GPS downtime, a new combined algorithm is proposed to provide virtual position and speed information to support an integrated positioning system, which is the application of ANN artificial neural network to improve accuracy when GPS is lost. The article focuses on improving the position accuracy and speed of carrier ships when GPS is lost. This issue is still new in Vietnam, and little has been published. The authors propose a solution to place a micromechanical gyroscope to measure angular speed, an accelerometer to measure apparent acceleration, and a magnetometer on the device to combine algorithms to solve the problem just mentioned above.


[1]. Bo, Fu, Liu Li, Bao Jiuhong. "GPS/INS/speed log integrated navigation system based on MAKF and priori velocity information." Information and Automation (ICIA), IEEE International Conference on. IEEE, (2013). DOI: https://doi.org/10.1109/ICInfA.2013.6720269

[2]. Grewal, Mohinder S., Lawrence R. Weill, and Angus P. Andrews. “Global positioning systems, inertial navigation, and integration”. John Wiley & Sons, (2007). DOI: https://doi.org/10.1002/0470099720

[3]. Enkhtur, Munkhzul, Seong Yun Cho, and Kyong-Ho Kim. "Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System." ETRI Journal 35.5: 943-946, (2013). DOI: https://doi.org/10.4218/etrij.13.0212.0540

[4]. Loebis D., Sutton R., Chudley J., Naeem W, “Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system”, Control engineering practice, 12(12), pp.1531-1539, (2004). DOI: https://doi.org/10.1016/j.conengprac.2003.11.008

[5]. Sharaf R. and Noureldin A, “Sensor integration for satellite-based vehicular navigation using neural networks”, IEEE transactions on neural networks, 18(2), pp.589-594, (2007). DOI: https://doi.org/10.1109/TNN.2006.890811

[6]. R. Sharaf, A. Noureldin, A. Osman, and N. El-Sheimy, ‘‘Online INS/GPS integration with a radial basis function neural network,’’ IEEE Aerosp. Electron. Syst. Mag., vol. 20, no. 3, pp. 8–14, (2005). DOI: https://doi.org/10.1109/MAES.2005.1412121

[7]. Abdel-Hamid, Walid, Aboelmagd Noureldin, Naser El-Sheimy. "Adaptive fuzzy prediction of low-cost inertial-based positioning errors." Fuzzy Systems, IEEE Transactions on 15.3: 519-529, (2007). DOI: https://doi.org/10.1109/TFUZZ.2006.889936

[8]. Noureldin, Aboelmagd, Ahmed Osman, Naser El-Sheimy. "A neuro-wavelet method for multi-sensor system integration for vehicular navigation." Measurement science and technology 15.2: 404, (2004). DOI: https://doi.org/10.1088/0957-0233/15/2/013

[9]. Ahmed E. MahdiORCID, Ahmed AzouzORCID, Ahmed E. AbdallaORCID and Ashraf Abosekeen. “A Machine Learning Approach for an Improved Inertial Navigation System Solution” Sensors, 22(4), 1687, (2022). https://doi.org/10.3390/s22041687 DOI: https://doi.org/10.3390/s22041687

[10]. Zhao, S.; Zhou, Y.; Huang, T. “A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage”. Remote Sens. 14, 4494, (2022). DOI: https://doi.org/10.3390/rs14184494

[11]. Liu, Y.; Luo, Q.; Zhou, Y. “Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation”. IEEE Sens. J. 22, 8974–8985, (2022). DOI: https://doi.org/10.1109/JSEN.2022.3155166

[12]. M. S. Grewal, A. P. Andrews, “Kalman Filtering Theory and Practice”, Prentice Hall, (1993).

[13]. Trần Đức Thuận, Trương Duy Trung, Nguyễn Quang Vịnh, Nguyễn Sĩ Long, Trần Xuân Kiên, Bùi Hồng Huế, Nguyễn Văn Diên, “Xây dựng thuật toán xác định tham số định hướng cho phương tiện chuyển động trên cơ sở kết hợp con quay tốc độ góc với từ kế và gia tốc kế”. Tạp chí Nghiên cứu Khoa học và Công nghệ quân sự, (25), tr 7-16, (2013).

[14]. Hoàng Văn Long, Trần Đức Thuận, Nguyễn Quang Vịnh, “Ứng dụng bộ lọc Kalman phi tuyến kết hợp con quay đo tốc độ góc với gia tốc kế và từ kế xác định tham số định hướng cho thiết bị mang ở thời điểm phóng thiết bị bay”, Tạp chí Nghiên cứu Khoa học và Công nghệ quân sự, Vol.90, (2023). DOI: https://doi.org/10.54939/1859-1043.j.mst.90.2023.45-54

[15]. Simon Haykin. “Neural Networks and Learning Machines”. Pearson Education, Inc., Upper Saddle River, New Jersey 07458, (2008).



How to Cite

Hoàng Văn Long, Trần Đức Thuận, Nguyễn Quang Vịnh, and Nguyễn Đức Ánh. “Enhancing the Accuracy of Position and Speed Parameter Determination for Carrying Devices through Artificial Neural Networks”. Journal of Military Science and Technology, no. CAPITI, Apr. 2024, pp. 182-8, doi:10.54939/1859-1043.j.mst.CAPITI.2024.182-188.



Research Articles