The Situation Awareness Window: a Hidden Markov Model for analyzing Maritime Surveillance missions

Caelli, Terry, Mukerjee, Joyanto, McCabe, Andy and Kirszenblat, David 2021, The Situation Awareness Window: a Hidden Markov Model for analyzing Maritime Surveillance missions, Journal of defense modeling and simulation, pp. 1-9, doi: 10.1177/1548512920984370.

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Title The Situation Awareness Window: a Hidden Markov Model for analyzing Maritime Surveillance missions
Author(s) Caelli, TerryORCID iD for Caelli, Terry
Mukerjee, Joyanto
McCabe, Andy
Kirszenblat, David
Journal name Journal of defense modeling and simulation
Start page 1
End page 9
Total pages 9
Publisher SAGE Publications
Place of publication London, Eng.
Publication date 2021-01-21
ISSN 1548-5129
Keyword(s) Surveillance systems
Situation Awareness Window
Maritime Surveillance
Hidden Markov Models
Summary In recent years, the use of Maritime Surveillance (MS) systems has increased in both defense and civilian domains. A demanding workload is placed upon operators of these systems, including the need to perform simultaneous information fusion from a number of sources to enable rapid decision throughput based upon Situation Awareness (SA). We have developed a method to objectively encode, summarize, and analyze airborne MS crew activities to gain insights into what is attended to in the execution of surveillance requirements. We label this method the “Situation Awareness Window” (SAW), which integrates sensor and tactical information with kinematics to define key attention and decision components of the operators that emerge over the surveillance mission. The SAW is defined with respect to the objects that are surveyed, the surveillance activities, and their chronological order. A SAW Hidden Markov Model (SAW-HMM) operates upon the surveillance mission activity encoder, resulting in a probabilistic relationship between the attention switching across sensor types and surveyed objects over the entire mission. That is, to implement the SAW-HMM we encoded the selection of sensors and surveillance decisions using a novel “encoder-interface” that allows users to probe many different features, observations, and states of a given mission. Ultimately the SAW will provide automated, objective, and insightful post mission debriefing technologies for operators and mission planners to encapsulate task demands and SA features over the mission.
Language eng
DOI 10.1177/1548512920984370
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
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