UKF Applied for Position Estimation of Underwater-Beacon Precision

Yoon, Ba-Da; Yoon, Ha-Nul; Choi, Sung-He; Lee, Jang-Myung

doi:10.1007/978-3-642-33926-4_46

Cited by 3 publications

(2 citation statements)

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“…Several Bayes filter-based methods have been used to solve the localization problem [6] with sonar data. Among Bayes filters, the EKF and UKF have seen the most use in this domain (e.g., [26,27]). Karimi et al [12] showed that the EKF can outperform the UKF in their particular case.…”

Section: Pfmentioning

confidence: 99%

An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots

Hong,

Fulton,

Sattar

2018

Preprint

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This paper presents an evaluation of a number of probabilistic algorithms for localization of autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms, based on the principles of the Bayes filter, work by fusing bathymetry information with depth and altitude data from an AUV. Four different Bayes filter-based algorithms are used to design the localization algorithms: the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Marginalized Particle Filter (MPF). We evaluate the performance of these four Bayesian bathymetry-based AUV localization approaches under variable conditions and available computational resources. The localization algorithms overcome unique challenges of the underwater domain, including visual distortion and radio frequency (RF) signal attenuation, which often make landmark-based localization infeasible. Evaluation results on real-world bathymetric data show the effectiveness of each algorithm under a variety of conditions, with the MPF being the most accurate.

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Section: Pfmentioning

confidence: 99%

An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots

Hong,

Fulton,

Sattar

2018

Preprint

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“…Foss and Meland (2007) implemented the unscented filter for combining measurements from different sensors for an underwater vehicle and utilised numerical simulations for testing it. Yoon et al (2003) proposed a location estimation algorithm where the GPS and Inertial Navigation System (INS) signals were combined by the UKF. In another study (Benzerrouk et al, 2013), an adaptive cubature INS/GNSS integrated navigation system was presented for aerospace applications and evaluated in simulations.…”

Section: Introductionmentioning

confidence: 99%

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Shabani

2015

J. Navigation

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In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.2. Underwater Integrated Navigation System. 3. Error State Kalman Filter.4. Unscented Filtering.

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An Efficient Unscented Kalman Filter for Joint Angles Estimation and Control of Omni bundle with Noise

Rana

Gaur

Agarwal

et al. 2018

2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

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UKF Applied for Position Estimation of Underwater-Beacon Precision

Cited by 3 publications

References 4 publications

An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots

An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

An Efficient Unscented Kalman Filter for Joint Angles Estimation and Control of Omni bundle with Noise

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