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

Xu, Chao-Jiang; Wang, Yuhui; Ai, Wenlei

doi:10.1109/cgncc.2014.7007578

Cited by 1 publication

(1 citation statement)

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“…Regression analysis has also been employed to model regional terrorism risk (Chatterjee & Abkowitz, 2011) taking into consideration the population density and the number of critical infrastructures in each region. Moreover, for the threat evaluation in air defense operations, models based on Bayesian networks have been used for situation threat assessment (Kumar & Tripathi, 2016; Xu et al., 2014), while the importance of visual analytics in enhancing the threat assessment procedure has also been illustrated (Dahlbom & Helldin, 2013).…”

Section: Related Workmentioning

confidence: 99%

Real‐time threat assessment based on hidden Markov models

et al. 2023

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An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime‐ and terrorism‐related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model‐based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.

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