Ship track estimation using GPS data and Kalman Filter

Assaf, Mansour H.; Petriu, Emil M.; Voicu, Groza

doi:10.1109/i2mtc.2018.8409579

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Figure 6 shown below displays a simple nautical map of the Norwegian Coastal area. Experimental results show that marine vessels estimated tracks follow the same pattern as the observed ones with acceptable error difference and better compared to the estimated errors reported in [12].…”

Section: Fig 5 Observed/estimated Gps Coordinatessupporting

confidence: 60%

“…Case study 2 (Norway): We have collected and processed GPS data from marine vessel YTTERQY, a Norwegian passenger ship of 1201 gross tonnage and 49.9mx13.2m overall length x breadth extreme, built in 2015 [12]. Figure 6 shown below displays a simple nautical map of the Norwegian Coastal area.…”

Section: Fig 5 Observed/estimated Gps Coordinatesmentioning

confidence: 99%

“…Due to the non-linearity nature of the ship dynamic and kinematic problems, many proposed control design techniques are not competent enough for obtaining the optimal routes of marine vessels [12]. The application of the UKF based control techniques can be attractive and promising in this context.…”

Section: Introductionmentioning

confidence: 99%

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The Use of Kalman Filter Techniques for Ship Track Estimation

Assaf

Voicu

Petriu

2020

Wseas Transactions on Systems

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Unscented Kalman Filter (UKF) is a technique used in non-linear applications and dynamic systems identification (e.g. tracking marine vessels and ships) that require state and parameter estimation. This paper studies Kalman Filter (KF) based techniques for tracking ships using Global Positioning System (GPS) data. The present work proposes to exploit information from GPS sensors in order to track a ship in real-time. The absence and presence problem of a ship is handled by a applying KF theory to analyze GPS coordinates and compare current marine vessel routes to previously recorded ones. To study tracking performance, the system was implemented in C++ and simulation results demonstrate the feasibility and high accuracy of the proposed tracking method

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Section: Fig 5 Observed/estimated Gps Coordinatessupporting

confidence: 60%

Section: Fig 5 Observed/estimated Gps Coordinatesmentioning

confidence: 99%

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The Use of Kalman Filter Techniques for Ship Track Estimation

Assaf

Voicu

Petriu

2020

Wseas Transactions on Systems

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“…For example, Kuchler et al (2011) [54] confirmed the improvement in the estimation of the heave motion using the acceleration measurements and EKF filter. Furthermore, Assaf et al (2018) [55] in their study extended the use of EKF to discuss more general nonlinear estimation problems. The proposed algorithm was shown to be effective in tracking normal ship speed.…”

Section: Kalman Filtering Techniquesmentioning

confidence: 99%

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

Prpić-Oršić

et al. 2020

JMSE

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Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of induced nonlinear waves acting on a ship’s hull interfere with the systems. To generate control signals for actuators accurately, sensor measurements should be filtered and processed. Furthermore, for safe and green routing, the forces and moments acting on the ship’s hull should be taken into account in terms of their prediction. Thus, the design of such systems takes into account the problem of obtaining data about the directional wave spectra (DWS). Sensor systems individually cannot provide high accuracy and reliability, so their measurements need to be combined and complemented. Techniques based on the recursive Kalman filter (KF) are used for this purpose. When some measurements are unavailable, the estimation procedure should predict them and, based on the comparison of theoretical and measured states, reduce the error variance of the analyzed signals. Different approaches for improving estimation algorithms have evolved over the years with the indication of improvement. This paper gives an overview of the state-of-the-art estimation and filtering techniques for providing optimum estimation states in DP systems.

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“…GPS navigation system has become an ideal maritime navigation tool. The accuracy of navigation requirements varies from a few meters (on the beach, at the pier and in the river) to a few hundred meters (navigation on the way) [11]. Accurate GPS positioning using both pseudo-random measurement and measurement of carrier phase can lead to navigation of boats on the river and coastal without buoys [12].…”

Section: Introductionmentioning

confidence: 99%

Application of The KNN Algorithm in Determining the Orientation of The Probability Area Containing The Ship Position by GPS Systems on Hai Phong Coastal Area

Duong

Nguyen

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The need for navigation for the maritime industry is also very urgent. Today, most commercial boats have receivers and positioning signal receivers, giving an accuracy of several meters. For vessels arriving at, or passing through small canals, the ability to locate with an accuracy of less than 1 meter is necessary. In these cases, the use of DGPS technology is necessary. The determination of ship location and navigation depends on global satellite navigation systems, mainly GPS systems. In maritime practice, the position of the specified vessel is considered the most probable position and will be the center of the circle of probability area containing the ship position. However, the probability area containing the ship position is scalar, the radius of error of the circle of probability area depends on many factors, such as the deviation of geodetic system, the accuracy of the chart. Therefore, the determination of the most probable position with the highest accuracy is a quite complex problem. The demand for data processing is also greater; Machine Learning is thus contributing to solving this problem. In the framework of the article, the authors propose the application of the KNN algorithm to determine the orientation of the probability area containing the ship position with the most probable positions. The objective of this study is to improve efficiency and safety in maneuvering and navigation for sea vessels and testing for Hai Phong coastal area.

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Ship track estimation using GPS data and Kalman Filter

Cited by 8 publications

References 7 publications

The Use of Kalman Filter Techniques for Ship Track Estimation

The Use of Kalman Filter Techniques for Ship Track Estimation

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Application of The KNN Algorithm in Determining the Orientation of The Probability Area Containing The Ship Position by GPS Systems on Hai Phong Coastal Area

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