Wide area integrated maritime surveillance: an updated architecture with data fusion

Ding, Zhen; Kannappan, G.; Benameur, K.; Kirubarajan, T.; Farooq, M.

doi:10.1109/icif.2003.177391

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…In these studies, the surveillance area was restricted to the coastal regions and the combination of data from AIS, SAR, IR, video and radar was used in the fusion process to obtain the vessel tracks. Furthermore, there are some other works that focused on the fusion of both sensor and non-sensor data (Andler et al, 2009;Ding, Kannappan, Benameur, Kirubarajan, & Farooq, 2003;Fooladvandi et al, 2009;Lefebvre & Helleur, 2004;Mano, Georgé, & Gleizes, 2010;Riveiro & Falkman, 2009). For example, Lefebvre and Helleur (2004) and Riveiro and Falkman (2009) treated the expert's knowledge as the non-sensor data.…”

Section: Related Workmentioning

confidence: 99%

“…When the combination of anomalies exceeded a threshold, vessel status was informed to the user as an anomaly. The work presented by Ding, Kannappan, Benameur, Kirubarajan and Farooq (2003), proposed the architecture of a centralized integrated maritime surveillance system for the Canadian coasts. Sensors and databases included in this architecture were: HFSWR, ADS (Automatic Dependant Surveillance) reports, visual reports, information sources, microwave radar and radar sat.…”

Section: Related Workmentioning

confidence: 99%

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Open data for anomaly detection in maritime surveillance

Kazemi

Abghari

Lavesson

et al. 2013

Expert Systems with Applications

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Context Maritime Surveillance (MS) has received increased attention from a civilian perspective in recent years. Anomaly detection (AD) is one of the many techniques available for improving the safety and security in the MS domain. Maritime authorities utilize various confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new sources of data for MS. These newly identified data sources, which provide publicly accessible data, are the open data sources. Taking advantage of the open data sources in addition to the traditional sources of data in the AD process will increase the accuracy of the MS systems. Objectives The goal is to investigate the potential open data as a complementary resource for AD in the MS domain. To achieve this goal, the first step is to identify the applicable open data sources for AD. Then, a framework for AD based on the integration of open and closed data sources is proposed. Finally, according to the proposed framework, an AD system with the ability of using open data sources is developed and the accuracy of the system and the validity of its results are evaluated. Methods In order to measure the system accuracy, an experiment is performed by means of a two stage random sampling on the vessel traffic data and the number of true/false positive and negative alarms in the system is verified. To evaluate the validity of the system results, the system is used for a period of time by the subject matter experts from the Swedish Coastguard. The experts check the detected anomalies against the available data at the Coastguard in order to obtain the number of true and false alarms. Results The experimental outcomes indicate that the accuracy of the system is 99%. In addition, the Coastguard validation results show that among the evaluated anomalies, 64.47% are true alarms, 26.32% are false and 9.21% belong to the vessels that remain unchecked due to the lack of corresponding data in the Coastguard data sources. Conclusions This thesis concludes that using open data as a complementary resource for detecting anomalous behavior in the MS domain is not only feasible but also will improve the efficiency of the surveillance systems by increasing the accuracy and covering some unseen aspects of maritime activities.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Open data for anomaly detection in maritime surveillance

Kazemi

Abghari

Lavesson

et al. 2013

Expert Systems with Applications

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“…Mano, Georgé, and Gleizes (2010) proposed a system that collects data from radars and databases such as an environmental database, Lloyd's Insurance, and TF2000 Vessel DB. Ding, Kannappan, Benameur, Kirubarajan, and Farooq (2003) in turn proposed an architecture of a centralized integrated maritime surveillance system for Canadian coasts, fusing HFSWR, Automatic Dependant Surveillance reports, visual reports, and radar. The only research, which focuses on using open data available on the Internet for the purpose of maritime surveillance, is presented in (Kazemi et al, 2013).…”

Section: Data Enhancementmentioning

confidence: 99%

Data analysis in the maritime domain

Stróżyna¹,

Abramowicz²,

Węcel³

et al. 2022

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The book delivers a rich set of foundations, state-of-the-art knowledge, new approaches and methods for the purpose of anomalies detection, maritime traffic analysis as well as risk and reliability assessment. It addresses relevant research problems at the intersection of maritime transport in global economies, reliability and risk assessment, and information systems and data processing. The book provides a theoretical overview of available maritime data sources and approaches for maritime data analysis, as well as a set of novel tools and methods for maritime data retrieval, fusion, and analysis. The proposed methods are evaluated on real-life AIS data, covering the entire world and more than 200 thousands of vessels, illustrating how they may be used for anomaly detection and risk assessment. The primary audience of the book are researchers from the fields of computer science and maritime transport as well as logistics service providers, shipping companies and port authorities companies that need support in managing security, safety, and risk of maritime transport services in global economies by making use of large-scale data processing.

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“…Data fusion received from HF-OTHR and AIS provides extraction of paths which are not confirmed by AIS. This allows for detecting targets that are non-cooperative and which either pose a military threat or conduct illegal activities (such as smuggling) inside the EEZ [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Functional Dependencies as a Method of Choice for Fusion of AIS and OTHR Data

Mostafa

Vučetić²,

Stojkovic

et al. 2019

Sensors

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Maritime situational awareness at over-the-horizon (OTH) distances in exclusive economic zones can be achieved by deploying networks of high-frequency OTH radars (HF-OTHR) in coastal countries along with exploiting automatic identification system (AIS) data. In some regions the reception of AIS messages can be unreliable and with high latency. This leads to difficulties in properly associating AIS data to OTHR tracks. Long history records about the previous whereabouts of vessels based on both OTHR tracks and AIS data can be maintained in order to increase the chances of fusion. If the quantity of data increases significantly, data cleaning can be done in order to minimize system requirements. This process is performed prior to fusing AIS data and observed OTHR tracks. In this paper, we use fuzzy functional dependencies (FFDs) in the context of data fusion from AIS and OTHR sources. The fuzzy logic approach has been shown to be a promising tool for handling data uncertainty from different sensors. The proposed method is experimentally evaluated for fusing AIS data and the target tracks provided by the OTHR installed in the Gulf of Guinea.

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Wide area integrated maritime surveillance: an updated architecture with data fusion

Cited by 6 publications

References 5 publications

Open data for anomaly detection in maritime surveillance

Open data for anomaly detection in maritime surveillance

Data analysis in the maritime domain

Fuzzy Functional Dependencies as a Method of Choice for Fusion of AIS and OTHR Data

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