@INPROCEEDINGS{8729756,
  author={N. {Davari} and A. P. {Aguiar} and J. B. d. {Sousa}},
  booktitle={2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)}, 
  title={An AUV Navigation System Using an Adaptive Error State Kalman Filter Based on Variational Bayesian}, 
  year={2018},
  volume={},
  number={},
  pages={1-6},
  abstract={This paper presents an adaptive Kalman filtering approach that uses a variational Bayesian approximation method for robust navigation for Autonomous Underwater Vehicles. The integrated navigation system is composed of a strapdown inertial navigation system as main sensor along with Doppler Velocity Log (DVL), depthmeter and compass (all of them with different sampling rates) as complementary sensors. The proposed data integration multi-sensor and multi-rate adaptive algorithm considers unknown and time-varying statistical parameters of the measurement and process noises. The experimental tests show that the proposed algorithm is more accurate in terms of estimation of position, velocity and attitude, and it is more robust against outliers in the DVL measurements when compared with the extended Kalman filter and the error state Kalman filter approaches.},
  keywords={adaptive Kalman filters;approximation theory;Bayes methods;inertial navigation;nonlinear filters;remotely operated vehicles;underwater vehicles;AUV navigation system;adaptive error state Kalman filter;adaptive Kalman filtering approach;variational Bayesian approximation method;robust navigation;Autonomous Underwater Vehicles;integrated navigation system;strapdown inertial navigation system;main sensor;compass;complementary sensors;data integration multisensor;unknown time-varying statistical parameters;measurement;process noises;velocity;DVL measurements;extended Kalman filter;error state Kalman filter approaches;sampling rates;depth meter;Noise measurement;Navigation;Kalman filters;Mathematical model;Bayes methods;Covariance matrices;Measurement uncertainty;Integrated navigation system;autonomous underwater vehicle;error state Kalman filter;adaptive Kalman filter based on variational bayesian},
  doi={10.1109/AUV.2018.8729756},
  ISSN={2377-6536},
  month={Nov},}