Navigation of Autonomous Underwater Vehicle Using Extended Kalman Filter

2010 ◽  
pp. 1-9 ◽  
Author(s):  
Ranjan T.N ◽  
Arun Nherakkol ◽  
Gajanan Navelkar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle

scholarly journals Observer Kalman filter identification of an autonomous underwater vehicle

2004 ◽  
Vol 37 (10) ◽  
pp. 269-272
Author(s):  
A. Tiano ◽  
R. Sutton ◽  
A. Lozowicki ◽  
W. Naeem
Keyword(s):  
Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle

scholarly journals Moving Horizon Estimation for Cooperative Localisation with Communication Delay

2014 ◽  
Vol 68 (3) ◽  
pp. 493-510 ◽  
Author(s):  
Wei Gao ◽  
Jian Yang ◽  
Ju Liu ◽  
Hongyang Shi ◽  
Bo Xu
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Extended Kalman Filter ◽  
Acoustic Communication ◽  
Underwater Vehicle ◽  
Random Time ◽  
Computation Method ◽  
Moving Horizon Estimation ◽  
Underwater Environment ◽  
Simulation Results

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the “Lower Upper” (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm.

Strong Tracking Filter Based on Extended Kalman Filter for Data Processing of Underwater Vehicle

2011 ◽  
Vol 219-220 ◽  
pp. 569-573
Author(s):  
Ye Li ◽  
Zhen Lu ◽  
Yong Jie Pang
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Data Processing ◽  
Extended Kalman Filter ◽  
Underwater Vehicle ◽  
Underwater Navigation ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter ◽  
Navigation Information

A strong tracking filter based on suboptimal fading extended Kalman filter was proposed to ensure the perception for the motion state of underwater vehicles accurate in the paper. For the uncertainty of nonlinear system model, the strong tracking filter theory was introduced, orthogonality principle was put forward. Then suboptimal fading factor was pulled in, and extended Kalman filter for nonlinear system was established. The strong tracking filter was applied to data processing of underwater vehicle, and results indicate that it can effectively improve the accuracy and robustness of underwater navigation information.

scholarly journals Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions

2021 ◽  
Vol 11 (13) ◽  
pp. 5790
Author(s):  
Qiang Liu ◽  
Muguo Li
Keyword(s):  
Kalman Filter ◽  
Discrete Time ◽  
Ensemble Kalman Filter ◽  
Position Control ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Control Law ◽  
Hydrodynamic Parameters ◽  
Time Position ◽  
Noisy Conditions

This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV’s on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1–97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.

scholarly journals ESTIMASI GERAK TRANSLASI AUTONOMOUS UNDERWATER VEHICLE DENGAN ENSEMBLE KALMAN FILTER (EnKF)

2018 ◽  
Vol 2 (1) ◽  
pp. 41
Author(s):  
Teguh Herlambang ◽  
Subchan Subchan
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Degree Of Freedom

Penelitian dan pengembangan dari Autonomous Underwater Vehicle cukup banyak diantaranya terkait sistem kendali, navigasi dan hidrodinamika. Pada umunya persamaan gerak AUV adalah 6 derajat kebebasan/Degree of Freedom (DOF) yang terdiri dari gerak translasi (surge, sway, heave) dan gerak rotasi (roll, pitch, yaw). Pada paper ini dikembangkan metode estimasi gerak tranlasi dari ITSUNUSA AUV dengan metode Ensemble Kalman Filter. Pada paper ini juga dibandingkan berdasarakn pembangkian julah ensemble. Hasil simulasi menunjukkan bahwa yang terakurat adalah dengan membangkitkan 300 ensemble dengan error kecepatan untuk gerak surge adalah 0,082%, gerak sway 0.498% dan gerak heave 0.26%.

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