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INS-DVL navigation improvement using rotational motion dynamic model of AUV

Karmozdi, A ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/JSEN.2020.3007929
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. INS-DVL integration is a common method for underwater navigation. However, inherent errors of sensors, especially in MEMS IMUs, lead to inaccuracies in estimating the position and attitude. In this paper, dynamic motion model of AUV is used to improve MEMS INS-DVL navigation. In this method, which is called model-aided or model-based navigation, the information of the kinetic model of the vehicle (obtained from Newton-Euler equations) is used to improve the navigation performance. Previous model-aided navigation studies about AUVs have been focused on the translational dynamic model of vehicles. As the best of our knowledge, this paper is the first one which suggests using a rotational motion model of AUVs to improve the navigation performance and at the same time, reports successful implementation of method through field-testing. The utilized dynamic model, describes the rotational motion of AUV in a 3DOF form. Information extracted from the rotational dynamic model and the outputs of gyroscopes, will be fused in a secondary Kalman filter. So, before the fusion with auxiliary sensors in the main Kalman filter, the gyroscopes outputs have been improved by the rotational motion model. Therefore, the overall output will be better than the outputs of gyroscopes by itself. The main Kalman filter is a perturbation-based Kalman filter which is employed for data fusion. This algorithm is implemented on real data collected from field-test. The results show that this inexpensive method which requires no additional equipment was truly effective in improving navigation performance. Using this model, the accuracy of navigation was improved 5 to 10 times. © 2001-2012 IEEE
  6. Keywords:
  7. AUV ; Kalman filter ; Model-aided navigation ; Model-based navigation ; Underwater navigation ; Autonomous underwater vehicles ; Data fusion ; Dynamic models ; Gyroscopes ; Kalman filters ; Navigation ; Rotational flow ; Additional equipment ; Aided navigations ; Dynamic motion models ; Navigation performance ; Newton Euler equation ; Rotational dynamics ; Translational dynamics ; Underwater navigation ; Inertial navigation systems
  8. Source: IEEE Sensors Journal ; Volume 20, Issue 23 , 2020 , Pages 14329-14336
  9. URL: https://ieeexplore.ieee.org/document/9136748