Radar Plotting

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Di Zhang - One of the best experts on this subject based on the ideXlab platform.

  • classification of automatic Radar Plotting aid targets based on improved fuzzy c means
    Transportation Research Part C-emerging Technologies, 2015
    Co-Authors: Xinping Yan, Xiumin Chu, Di Zhang
    Abstract:

    Maritime ARPA, Automatic Radar Plotting Aid, systems often complicate navigation by mistaking channel structures and land objects for vessels in inland rivers and harbors. By using Fuzzy C-Means (FCM), it is possible to construct an artificial intelligence to classify and identify ARPA target types and calculate the possibility of a target being a real vessel based on the target’s speed over ground, vector over ground, and location. The membership functions of each attribute are constructed using statics, expert knowledge, and electronic chart information. The main difficulty in developing a successful FCM framework to achieve the previously stated goals is the determination of a proper method of calculating the classification number C and fuzzy coefficient m. Because the value of C for the case of ARPA targets classification is finite, the best C would be determined by assessing the Euclidean distance. The value of m is related to the discreteness of the evidence and results, which is evaluated using the Shannon entropy and the gain. A number of methods exist to properly evaluate the contributions from different forms of evidence so that the best m can be found using the tendentiousness of the evidence. In field testing, the improved FCM was able to accurately classify the ARPA targets, decrease the workload on the ship’s officer, and increase safety.

Xinping Yan - One of the best experts on this subject based on the ideXlab platform.

  • classification of automatic Radar Plotting aid targets based on improved fuzzy c means
    Transportation Research Part C-emerging Technologies, 2015
    Co-Authors: Xinping Yan, Xiumin Chu, Di Zhang
    Abstract:

    Maritime ARPA, Automatic Radar Plotting Aid, systems often complicate navigation by mistaking channel structures and land objects for vessels in inland rivers and harbors. By using Fuzzy C-Means (FCM), it is possible to construct an artificial intelligence to classify and identify ARPA target types and calculate the possibility of a target being a real vessel based on the target’s speed over ground, vector over ground, and location. The membership functions of each attribute are constructed using statics, expert knowledge, and electronic chart information. The main difficulty in developing a successful FCM framework to achieve the previously stated goals is the determination of a proper method of calculating the classification number C and fuzzy coefficient m. Because the value of C for the case of ARPA targets classification is finite, the best C would be determined by assessing the Euclidean distance. The value of m is related to the discreteness of the evidence and results, which is evaluated using the Shannon entropy and the gain. A number of methods exist to properly evaluate the contributions from different forms of evidence so that the best m can be found using the tendentiousness of the evidence. In field testing, the improved FCM was able to accurately classify the ARPA targets, decrease the workload on the ship’s officer, and increase safety.

Xiumin Chu - One of the best experts on this subject based on the ideXlab platform.

  • classification of automatic Radar Plotting aid targets based on improved fuzzy c means
    Transportation Research Part C-emerging Technologies, 2015
    Co-Authors: Xinping Yan, Xiumin Chu, Di Zhang
    Abstract:

    Maritime ARPA, Automatic Radar Plotting Aid, systems often complicate navigation by mistaking channel structures and land objects for vessels in inland rivers and harbors. By using Fuzzy C-Means (FCM), it is possible to construct an artificial intelligence to classify and identify ARPA target types and calculate the possibility of a target being a real vessel based on the target’s speed over ground, vector over ground, and location. The membership functions of each attribute are constructed using statics, expert knowledge, and electronic chart information. The main difficulty in developing a successful FCM framework to achieve the previously stated goals is the determination of a proper method of calculating the classification number C and fuzzy coefficient m. Because the value of C for the case of ARPA targets classification is finite, the best C would be determined by assessing the Euclidean distance. The value of m is related to the discreteness of the evidence and results, which is evaluated using the Shannon entropy and the gain. A number of methods exist to properly evaluate the contributions from different forms of evidence so that the best m can be found using the tendentiousness of the evidence. In field testing, the improved FCM was able to accurately classify the ARPA targets, decrease the workload on the ship’s officer, and increase safety.

Mthembu, Sibusisiwe Nothando. - One of the best experts on this subject based on the ideXlab platform.

  • Navigating the complex maritime cyber regime: a review of the international and domestic regulatory framework on maritime cyber security.
    2019
    Co-Authors: Mthembu, Sibusisiwe Nothando.
    Abstract:

    Masters Degree. University of KwaZulu-Natal, Durban.Modern shipping companies are reliant on the proliferation of refined technological advancements such as Electric Chart Display and Information Systems (ECDIS), Automatic Identification System (AIS), Global Maritime Distress and Safety System (GMDSS), Compass (Gyro, fluxgate, GPS and others), Computerised Automatic Steering Systems, Voyage Data Recorders – “Black box” (VDR), Radio Direction and Ranging or Automatic Radar Plotting Aid (Radar/ARPA). These technological advancements are vulnerable to cyber security threats. The prevalence of maritime cyber security incidents is increasing worldwide therefore it is imperative for the maritime industry to have legal regime in place that adequately regulates these cyber security threats. This dissertation undertakes a critical analysis of the legal framework governing maritime cyber security and the adequacy in combating maritime cyber threats. The first chapter will provide an introduction and background to maritime cyber security. The second chapter focuses on the different threats and vulnerabilities to maritime cyber security. In addition to this reference will be made to the types of cybercrimes and their possible ramifications. The third chapter will analyse the International regulatory regimes in place, regional regulatory framework and South Africa’s domestic laws regulating maritime cyber security. In the fourth Chapter a determination will be made as to the existence and adequacy of the law in combating maritime cyber threats and crimes. A conclusion will be derived from the findings of this dissertation, and recommendation will be submitted The purpose of this study is to establish whether, (a) the existing law applies to maritime cyber security threats at all, and, if so, what is the extent of the existing laws applicability to maritime cyber security threats? (b) whether the domestic and international legal framework is adequate, in respect to enforcement and comprehensiveness, to address/respond to maritime cyber security threats? and (c) whether it is necessary to establish new regulations to address maritime cyber security or develop existing laws?Dedication & Acknowledgement are in isiZulu

Noack Thoralf - One of the best experts on this subject based on the ideXlab platform.

  • A pilot study of the advantage of Radar image data over ARPA based position and bearing
    2014
    Co-Authors: Heymann Frank, Banyś Paweł, Noack Thoralf
    Abstract:

    In this paper, we analyze the added value of maritime Radar image data for tracking and classification of target vessels. The current automatic Radar Plotting aid (ARPA) only provides a single point of the acquired Radar target. A Radar image on the other hand contains two dimensional information on the whole surrounding area as well as the 2D projection of the target itself. The paper starts with a theoretical discussion of possible additional values of using Radar image data instead of ARPA based distance and bearing information. In addition, an astrophysical tool is used to extract targets and estimate their minor and major semi-axes of the best fitting ellipse. Finally, we analyze the stability of the ellipsoidal parameters during the tracking of targets. This is done using the Automatic Identification System (AIS) as the primary source of target vessel positioning. With the AIS data it is possible to choose the closest ellipse on the Radar image. Repeating this procedure until the AIS target leaves the Radar range makes the stability analysis possible

  • The role of integrity for maritime traffic situation assessment
    Akademia Morska w Szczecinie, 2011
    Co-Authors: Banyś Paweł, Noack Thoralf, Gewies Stefan, Engler Evelin
    Abstract:

    Starting from the E-Navigation concept of IMO this paper will give an overview about integrity in maritime traffic awareness. Derived from a DLR study a concept is presented which introduces the provision of integrity maritime traffic awareness. This is based on common techniques like Automatic Radar Plotting Aid (ARPA) and Automatic Identification System (AIS) and a PNT-Unit which gives ship position, navigation, and time with integrity