Extended and Unscented Kalman filters for parameter estimation of an autonomous underwater vehicle

Sabet, Mohammad Taghi; Sarhadi, Pouria; Zarini, Mostafa

doi:10.1016/j.oceaneng.2014.09.013

Cited by 70 publications

(20 citation statements)

References 17 publications

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“…While in PCA-DR we do not assume knowledge of the water current, some approaches have been suggested to compensate for a given or directly measured water current. The work in [25] takes into account the water current along the water column during the DR operation. The solution offered directly measures the water current using a Doppler velocity logger or an acoustic Doppler current profiler positioned on an AUV, and integrates this data in the filtering scheme.…”

Section: Approaches For Underwater Dead Reckoningmentioning

confidence: 99%

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Klein

Diamant

2020

JMSE

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Between external position updates, the most useful technique for trajectory estimation of a submerged drifter is dead reckoning (DR). These devices drift with the water current to measure the current’s velocity or to observe physical phenomena. We focus on the specific but important case of when the drifter, due to its size and shape, experiences acceleration by the water current, an effect that must be taken into account during the DR. The force induced by the water current over the drifter is translated into a shift in the heading direction, thus creating a horizontal (sideslip) and a vertical (angle of attack) directional angles between the drifter’s moving direction and its body frame. In this paper, we extend and modify techniques used for pedestrian DR and propose PCA-DR: a principle component analysis-based DR algorithm to estimate the directional angles. Used for cases where the water current is significant such that its force induces acceleration over the drifter and used only for short time periods of a few seconds between navigation fixes, PCA-DR uses acceleration measurements only and does not assume knowledge of the drifter’s dynamics. Instead, as part of the DR process, PCA-DR estimates the directional angles induced by the water current. Compared to the traditional DR approach, our results demonstrate good navigation performance. A designated sea experiment demonstrates the applicability of PCA-DR in a realistic sea environment.

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Section: Approaches For Underwater Dead Reckoningmentioning

confidence: 99%

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Klein

Diamant

2020

JMSE

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“…The techniques of AUV localization usually can be classified to two categories, i.e., inertial/dead reckoning system and the acoustic transponders and modem system. The inertial/dead reckoning system uses inertial measurement unit (IMU) or Doppler Velocity Logs (DVL) to estimate the vehicle position [11]. However, all of the methods in this category have position error growth that is unbounded.…”

Section: Introductionmentioning

confidence: 99%

Accurate Closed-Form Solution for Moving Underwater Vehicle Localization Using Two-Way Travel Time

et al. 2020

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Position information is essential for an underwater vehicle that can affect the performance of many other applications. Vehicle motion during the two-way travel time (TWTT) using an acoustic positioning system can affect the localization accuracy of the estimators based on the traditional static model. A new time measurement model for moving vehicle localization is presented, which compensates for the vehicle motion. The Cramér–Rao lower bounds (CRLBs) of the proposed model are derived for two different cases, where the depth of the underwater vehicle is unknown or known. Then, closed-form solutions for the two cases using the proposed model are derived and the solutions are shown analytically to reach the CRLBs. Simulations collaborate the theoretical performance of the proposed estimators and the moving model significantly improves the localization accuracy in comparison with the static model.

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“…The natural advantage of UKF is the third-order Taylor expansion approximate used to obtain the conditional mean and variance for Gaussian noises accurately, which does not rely on any linearization processing for state predictions and covariance. Consequently, compared with the robust EKF, the UKF has been used in numerous industrial applications, such as state estimation and remaining useful life predict for rotary machines [9][10][11], battery health management [12][13], target tracking and satellite-aided navigation [14][15], among others. However, in real industrial application, the unknown process noise is frequency encountered, and the accumulated state estimation errors, in the case of the process noise covariance is not obtained accurately, become more and more serious.…”

Section: Introductionmentioning

confidence: 99%

A hybrid adaptive unscented Kalman filter algorithm

2019

IJEM

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In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for state and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UKF based MAP and based ML, the proposed algorithm provides better convergence and stability. KEY WORDS:hybrid adaptive; unscented Kalman filtering; maximum a posteriori; maximum likelihood criterion.

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Extended and Unscented Kalman filters for parameter estimation of an autonomous underwater vehicle

Cited by 70 publications

References 17 publications

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Dead Reckoning for Trajectory Estimation of Underwater Drifters under Water Currents †

Accurate Closed-Form Solution for Moving Underwater Vehicle Localization Using Two-Way Travel Time

A hybrid adaptive unscented Kalman filter algorithm

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