Automatic Identification System (AIS) Dynamic Data Estimation Based on Discrete Kalman Filter (KF) Algorithm

Jaskólski, Krzysztof

doi:10.5604/01.3001.0010.6747

Cited by 26 publications

(15 citation statements)

References 2 publications

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“…Live AIS data can be used to predict the motion of ships using a linear discrete-time KF as shown by Jaskolski (2017). Our approach is a nonlinear observer with proven global exponential stability (GES) properties using a generic ship model, which does not require information about the ship model parameters.…”

Section: Motion Predictionmentioning

confidence: 99%

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

Fossen¹,

Fossen²

2018

MIC

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This paper addresses the problem of ship motion estimation using live data from Automatic Identification Systems (AIS). A globally exponentially stable observer for visualization and motion prediction of ships has been designed. Instead of using the extended Kalman filter (EKF) to deal with the kinematic nonlinearities the eXogenous Kalman Filter (XKF) is applied and by this global stability properties are proven.The proposed observer was validated using live AIS data from the Trondheim harbor in Norway and it was demonstrated that the observer tracks ships in real time. It was also demonstrated that the observer can predict the future motion of ships.The motion prediction capabilities are very useful for decision-support systems since this can be used to improve situational awareness e.g. for manned and unmanned autonomous ships that operate in common waters.

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Section: Motion Predictionmentioning

confidence: 99%

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

Fossen¹,

Fossen²

2018

MIC

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“…AIS information including position and velocity of each vessel in discrete and irregular manner was regarded as a conventional apparatus of vessel surveillance, but its latest application includes combined research with remotely sensed data such as SAR [22] and electro-optical (EO) satellite images [23]. As the majority of AIS information was not acquired in the desired SAR acquisition time, a number of interpolation procedures were presented to estimate precise and accurate AIS information which corresponds to the SAR and EO images: linear interpolation, circular interpolation [24] and Kalman filter [25]. From these studies conducted, high detection performance and robust operation of SAR image-based vessel detector via training database were anticipated.…”

Section: Restoration Of Authentic Position Of Unidentified Vessels In...mentioning

confidence: 99%

“…The Kalman filter was introduced to the interpolated position and velocity to eradicate the intrinsic device error from the AIS and VPASS sensors. The form of Kalman filter followed that of [25].…”

Section: B Input Data Preprocessingmentioning

confidence: 99%

Restoration of Authentic Position of Unidentified Vessels in SAR Imagery: A Deep Learning Based Approach

Song

Kim

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Enhancement of vessel detection performance in SAR images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance however, presentation of authentic position of vessels was essential. Accordingly, this study aimed to propose an algorithm which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: AIS and VPASS information. Two different analyses regarding the vessel detection output utilization were therefore presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via (i) velocity estimator complementing the conventional FrFT velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and (ii) measuring the vessel orientation angle from Radon Transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to CNN-based vessel detection algorithms demonstrating ideal detection performances.

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“…Using onboard navigation systems, it is possible to define the coordinates of one's ship [1]. In relation to the other ships, one must rely on radio-navigational systems, e.g., the Automatic Identification System (AIS) [2]. Safe navigation based on reliable and accurate positioning is still an unsolved problem.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

Jaskólski

Marchel

Felski

et al. 2021

Sensors

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To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.

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Automatic Identification System (AIS) Dynamic Data Estimation Based on Discrete Kalman Filter (KF) Algorithm

Cited by 26 publications

References 2 publications

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

eXogenous Kalman Filter (XKF) for Visualization and Motion Prediction of Ships using Live Automatic Identification System (AIS) Data

Restoration of Authentic Position of Unidentified Vessels in SAR Imagery: A Deep Learning Based Approach

Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

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