Situation assessment via Bayesian belief networks

Das, Subrata; Grey, R.; Gonsalves, Paul

doi:10.1109/icif.2002.1021218

Cited by 43 publications

(22 citation statements)

References 2 publications

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“…Moreover, random variables are continuous (state), discrete (type), and time dependent. The graphical models represent an interesting formalism in object-of-interest detection (OID) and have already been used in similar topics [26][27][28][29]. Graphical models are traditionally used to represent dependency relations between a set of N random variables.…”

Section: Object-of-interest Detectionmentioning

confidence: 99%

Situation Assessment: An End-to-End Process for the Detection of Objects of Interest

Pollard

Rombaut

Pannetier

2013

IEEE Trans. Aerosp. Electron. Syst.

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Section: Object-of-interest Detectionmentioning

confidence: 99%

Situation Assessment: An End-to-End Process for the Detection of Objects of Interest

Pollard

Rombaut

Pannetier

2013

IEEE Trans. Aerosp. Electron. Syst.

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“…Using the Bayesian network approach, many scholars (Chien et al, 2002;Das et al, 2002;Koivisto and Sood, 2004) modify the probability of a research problem with historical data and expertise, and infer the probability of results conditioned on the occurrence of a certain event. However, the Bayesian network has certain limits.…”

Section: Introductionmentioning

confidence: 99%

A disaster warning model for a spacecraft launch based on information fusion and network inference

Xuejun

Chen

et al. 2012

IJRAM

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The launch of spacecraft should synthesise various incomplete and uncertain symptoms of extreme events to dynamically assess the possibility of disaster during the launch readiness process. However, there are still many technical problems in disaster assessment of spacecraft launch engineering. For example, the problem of how to effectively use the disaster symptoms is not addressed well yet and the fact that group decision-making can be easily lead to the minority opinion being ignored. Based on the extreme event symptom obtaining technique, a disaster warning model combining both virtual and real evidence is built through information fusion and network inference, using evidence theory and Bayesian theory. Using information fusion, the proposed model shows good performance with regard to complementary evidence, A disaster warning model for a spacecraft launch 143 elimination of data redundancy and reduction of uncertainty. Using symptom information-based network inference, this model guarantees the identification of the correct minority. Finally, the effectiveness, correctness and feasibility of the model are verified by analysis of the 'Challenger' space shuttle explosion.Keywords: risk assessment management; disaster warning model; spacecraft launch engineering; information fusion; network inference; extreme events symptoms.Reference to this paper should be made as follows: Xuejun, D., Yingwu, C., Jianwei, M. and Ming, L. (2012) 'A disaster warning model for a spacecraft launch based on information fusion and network inference', Int.

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“…In [7], S. Das presented a concept of two-level information fusion in Bayesian network for situation assessment. Threat assessment process was presented by F. Johansson [8] who also described a threat assessment in an air surveillance context with parameters that had been suggested.…”

Section: Introductionmentioning

confidence: 99%

Situation assessment in the warships-airplanes joint operation based on parameter learning in Bayesian network

Wang

2014

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference

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This paper presents a situation assessment system structure of the warships-airplanes joint operation and a new situation assessment algorithm based on parameter learning in .Bayesian network. Previously, situation assessments are usually operated by using a normal kind of Bayesian network with immutable probability distribution, which may cause the evaluation is too subjective. Therefore, a Bayesian assessment network with parameter learning is considered for a more objective result of the situation assessment. Then a new assessment algorithm of the parameter learning is discussed, which will improve the performance of the assessment. Finally, a model of the battlefield situation assessment of the warships-airplanes joint operation is presented. Its situation assessment simulation results verify the effectiveness of the proposed algorithm.

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Situation assessment via Bayesian belief networks

Cited by 43 publications

References 2 publications

Situation Assessment: An End-to-End Process for the Detection of Objects of Interest

Situation Assessment: An End-to-End Process for the Detection of Objects of Interest

A disaster warning model for a spacecraft launch based on information fusion and network inference

Situation assessment in the warships-airplanes joint operation based on parameter learning in Bayesian network

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