Data integrity assessment for maritime anomaly detection

Iphar, Clément; Ray, Cyril; Napoli, Aldo

doi:10.1016/j.eswa.2020.113219

Cited by 41 publications

(31 citation statements)

References 32 publications

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“…Electronics 2021, 10, x FOR PEER REVIEW Other solution of AIS was presented in [27]. Authors proposed using the ioned artificial intelligence method of data integrity assessment based on set of r work was developed in cooperation with representatives of French military uni ers of the French Navy and cadets of the French naval academy.…”

Section: Application Of Neural Network In Object Locationmentioning

confidence: 99%

“…where µ is a location parameter, equal to the distribution mean and Other solution of AIS was presented in [27]. Authors proposed using the old-fashioned artificial intelligence method of data integrity assessment based on set of rules.…”

Section: Application Of Neural Network In Object Locationmentioning

confidence: 99%

“…According to the method described in [30], it is assumed that there are K units (clusters) with estimated values of µ and Other solution of AIS was presented in [27]. Authors proposed using the old-fashioned artificial intelligence method of data integrity assessment based on set of rules.…”

Section: Application Of Neural Network In Object Locationmentioning

confidence: 99%

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Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.

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Section: Application Of Neural Network In Object Locationmentioning

confidence: 99%

Section: Application Of Neural Network In Object Locationmentioning

confidence: 99%

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Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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“…Although AIS has played a significant role in maritime transport-related research with its enormous amount of data, data error is likely to occur due to various factors [4,5]. Those incorrect data lead to inaccurate conclusions in trajectory analysis, which is crucial for further applications.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Guo

Mou

Chen

et al. 2021

JMSE

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With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

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“…Moreover, the large-scale AIS datasets have been stored at regional or national data centers, which can be accessible by users in request (note that users may need to pay for accessing the AIS data). Previous studies suggested that AIS data quality imposes significant influence on the maritime traffic safety analysis, and thus improving AIS data quality has become an active topic in the maritime community [13,14].…”

Section: Introductionmentioning

confidence: 99%

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

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Data integrity assessment for maritime anomaly detection

Cited by 41 publications

References 32 publications

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

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