Object-oriented sensor data fusion for wide maritime surveillance

Fischer, Yvonne; Bauer, Alexander

doi:10.1109/wssc.2010.5730244

Cited by 38 publications

(28 citation statements)

References 12 publications

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“…respective consistency conditions. For World Model and Background Knowledge, different realizations exist (e.g., [3,8]). For this contribution, an abstract but more formal view on information representation in the OOWM is taken.…”

Section: Components Of the Object-oriented World Modelmentioning

confidence: 99%

“…A global PMR detection measure at the point of time t = t x can for example be defined on the basis of the model correspondence (8) according to Fig. 13.…”

Section: Global Measuresmentioning

confidence: 99%

“…The OOWM has previously been employed in different application domains, including autonomous service robotics (e.g., [2]), maritime situation assessment (e.g., [8]), and video surveillance [7]. The OOWM is designed to be a probabilistic information management framework able to organize acquired information about its environment.…”

Section: Introductionmentioning

confidence: 99%

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Extending adaptive world modeling by identifying and handling insufficient knowledge models

Kuwertz

Beyerer

2016

Journal of Applied Logic

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Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach

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Section: Components Of the Object-oriented World Modelmentioning

confidence: 99%

“…A global PMR detection measure at the point of time t = t x can for example be defined on the basis of the model correspondence (8) according to Fig. 13.…”

Section: Global Measuresmentioning

confidence: 99%

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Extending adaptive world modeling by identifying and handling insufficient knowledge models

Kuwertz

Beyerer

2016

Journal of Applied Logic

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“…Bauer et al (2009) developed a data fusion architecture based on the JDL (Joint Directors of Laboratories) data fusion process model, see Steinberg et al (1999). An application of the OOWM for wide area maritime surveillance is presented in Fischer & Bauer (2010). Another application is to provide humanoid robots with a memory structure based on a complete Bayesian network, see for example Baum et al (2010) or Belkin (2009.…”

Section: Domains Of the World Modelingmentioning

confidence: 99%

World Modeling for Autonomous Systems

Gheta

Heizmann

Belkin

et al. 2010

KI 2010: Advances in Artificial Intelligence

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“…Latest scientific and technological developments on coastal monitoring and operational oceanography have provided the opportunity to build more complex and integrated decision support systems for coastal risk management. The increasing operational predictive capacity of marine weather conditions (Hurlburt et al, 2009;Schiller, 2011) and better knowledge of the fate and behaviour processes of pollutants spilt at sea or coastal zones (Fingas, 2015;Johansen et al, 2015;Zhao et al, 2014a, b;Gong et al, 2014), together with the presence of advanced surveillance monitoring tools (Fischer and Bauer, 2010), can be integrated in order to provide a safer support for decision making in emergency or planning issues associated with pollution risks.…”

mentioning

confidence: 99%

Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

Fernandes¹,

Braunschweig²,

Lourenço³

et al. 2016

Ocean Sci.

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Abstract. The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events.A combined methodology to dynamically estimate time and space variable individual vessel accident risk levels and shoreline contamination risk from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area -the Portuguese continental shelf -with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time, or as an alternative, a correction factor based on vessel distance from coast. Shoreline risks can be computed in real time or from previously obtained data.Results show the ability of the proposed methodology to estimate the risk properly sensitive to dynamic metocean conditions and to oil transport behaviour. The integration of meteo-oceanic + oil spill models with coastal vulnerability and AIS data in the quantification of risk enhances the maritime situational awareness and the decision support model, providing a more realistic approach in the assessment of shoreline impacts. The risk assessment from historical data can help finding typical risk patterns ("hot spots") or developing sensitivity analysis to specific conditions, whereas real-time risk levels can be used in the prioritization of individual ships, geographical areas, strategic tug positioning and implementation of dynamic risk-based vessel traffic monitoring.

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Object-oriented sensor data fusion for wide maritime surveillance

Cited by 38 publications

References 12 publications

Extending adaptive world modeling by identifying and handling insufficient knowledge models

Extending adaptive world modeling by identifying and handling insufficient knowledge models

World Modeling for Autonomous Systems

Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions

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