Position estimation of Touristant ASV using ensemble Kalman filter

Nurhadi, Hendro; Herlambang, Teguh; Adzkiya, Dieky

doi:10.1063/1.5138367

Cited by 13 publications

(9 citation statements)

References 3 publications

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“…Imagine how they can reach Indonesia without transportation. On the other hand, Indonesia's geographical condition is as an archipelagic and maritime country [1], meaning it surely makes maritime tourism transportation an important thing in connecting among regions in Indonesia [2].…”

Section: Introductionmentioning

confidence: 99%

H-infinity for autonomous surface vehicle position estimation

Herlambang

Rahmalia

Suryowinoto

et al. 2022

J. Phys.: Conf. Ser.

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Indonesia is a country with a very prominent marine charm, and it is the largest archipelago in the world, covering about 50% of the coral triangle area, providing marine tourist resorts. Therefore tourism development is quite promising. For that purpose, simple boats are of necessity for tourists to enjoy the beauty of such marine tourism. One solution to support such marine tourism sector is the availability of Autonomous Surface Vehicle (ASV). This vehicle will be the interest of this paper. This study used Touristant ASV with dimensions of 1.5 m in diameter, 4 meters in length and 1.3 meters in height. The purpose of this paper is to conduct a study with a focus on the estimation of ASV position with ASV motion influenced by wind speed and wave height by applying the H-infinity method. The position error generated from the simulation shows that the position error has an accuracy of about 96%.

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Section: Introductionmentioning

confidence: 99%

H-infinity for autonomous surface vehicle position estimation

Herlambang

Rahmalia

Suryowinoto

et al. 2022

J. Phys.: Conf. Ser.

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“…The methods used for estimation were H-Infinity and Ensemble Kalman Filter (EnKF). Several studies have developed the Kalman Filter development method for stock estimation [2,3,9], world oil price [4,5,6] or company profit [7,8].…”

Section: Introductionmentioning

confidence: 99%

Profitability estimation of XYZ company using H-infinity and Ensemble Kalman Filter

Anshori

Herlambang

Katias

et al. 2022

J. Phys.: Conf. Ser.

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One strategy for a company to develop and grow bigger and stronger in competition is by business expansion. According to Moin (2010:2), there are two strategic ways to maintain and expand a business, that is, an internal expansion strategy by building a new business or business unit from scratch and an external expansion strategy in the form of a business merger by buying an existing company through a merger, acquisitions and consolidations which are fast-track to new markets. Profitability is a factor that gets important attention because it can attract investors to make investment in the company. Profit is the gab beetwen revenue and expenses in a certain period in a company. It has very important role for either internal or external of the company. One way to reduce the risk of company losses is by using an estimation method to find out the company’s estimated profit for the next month or next year. In this study the estimation method for travel company was used, that is, by using the Ensemble Kalman Filter (EnKF) and H-infinity, as a policy making chart in order to maintain and increase company profits. Based on the analysis of results computation with 100, 200 and 300 iterations, it showed an error of less than 3%.

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“…Then Schoniger et al [10] estimated parameter of hydraulic tomography using ensemble Kalman filter (EnKF). In Nurhadi et al [11] conducted a research related to the estimation of ASV position and motion due to the influence of wind speed and wave height by applying the EnKF. The results of the application of the EnKF algorithm showed the smallest position error and a high degree of accuracy [11].…”

Section: Introductionmentioning

confidence: 99%

Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter

Nurhadi

Kiki

Adzkiya

et al. 2022

IJECE

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<span lang="EN-US">Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.</span>

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Position estimation of Touristant ASV using ensemble Kalman filter

Cited by 13 publications

References 3 publications

H-infinity for autonomous surface vehicle position estimation

H-infinity for autonomous surface vehicle position estimation

Profitability estimation of XYZ company using H-infinity and Ensemble Kalman Filter

Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter

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