Although significant effort has occurred into developing realistic simulation environments for maritime surveillance, relatively little attention has been given to objectively summarizing and providing platforms for querying the mission simulation outputs for post-mission debriefing purposes. In this paper we adopt recently proposed data analytic ideas for this context and illustrate the proposed approach using multi-layered statistical methods from visualization to Bayesian network-based data interpretation models. Analyzing example missions in these terms demonstrates the potential use for such technologies for objective and insightful post-mission analyses.
GarcíaJGómez-RomeroJPatricioMAet al. On the representation and exploitation of context knowledge in a harbor surveillance scenario. In: 2011 proceedings of the 14th international conference on information fusion (FUSION), Chicago, IL, 5–8 July 2011, pp.1–8. Piscataway, NJ: IEEE.
2.
RogovaGLScottPDLollettCet al. Reasoning about situations in the early post-disaster response environment. In: 2006 9th International Conference on Information Fusion, Florence, 10–13 July 2006, pp.1–8. Piscataway, NJ: IEEE.
RabinerLR. A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE1989; 77: 257–286.
7.
DomingosPLowdD. Markov logic: an interface layer for artificial intelligence. Synth Lect Artif Intell Mach Learn2009; 3: 1–155.
8.
KrügerMZieglerJHellerK. A generic bayesian network for identification and assessment of objects in maritime surveillance. In: 2012 15th International Conference on Information Fusion (FUSION), Singapore. 9–12 July 2012, pp.2309–2316. Piscataway, NJ: IEEE.
9.
KazemiSAbghariSLavessonNet al. Open data for anomaly detection in maritime surveillance. Exp Syst Appl2013; 40: 5719–5729.
10.
NilssonMVan LaereJZiemkeTet al. Extracting rules from expert operators to support situation awareness in maritime surveillance. In: 2008 11th International Conference on Information Fusion, 30 June–3 July 2008, pp.1–8. Piscataway, NJ: IEEE.
11.
VardjanMPorekarJ. Maritime surveillance scenario simulator. Int J Model Optimiz2012; 2: 449.
12.
LiZHGBaZ. Development and application of flight simulation management information system based on web technology. J Appl Sci2013; 13: 3936–3940.
13.
McDonaldMLUptonSCSeymourCNet al. Enhancing the analytic utility of the synthetic theater operations research model (storm). Technical report, DTIC Document, 2014.
14.
SanchezSM. Simulation experiments: Better data, not just big data. In: Proceedings of the 2015 Winter Simulation Conference, Savanah, GA, 7–10 December 2014, pp. 805-816. Piscataway, NJ: IEEE.
15.
SheehanPMukerjeeJ. Application of desktop human in the loop simulation to study air operations. In: 21st International Congress on Modelling and Simulation (MODSIM2015), Broadbeach, 29 November–4 December 2015. Modelling and Simulation Society of Australia and New Zealand Inc. pp.870–875.
16.
MevassvikOMBråthenKHansenBJ. A simulation tool to assess recognized maritime picture production in c2 systems. In: Proceedings of the 6th International Command and Control Research and Technology Symposium, Quebec City, 21 June 2011. The International Command and Control Institute.
17.
MonperrusMJaozafyFMarchalotGet al. Model-driven simulation of a maritime surveillance system. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp.361–368.
18.
PrevotTSmithNPalmerEet al. The Airspace Operations Laboratory (AOL) at NASA Ames Research Center. In: Proceedings of the AIAA Modeling and Simulation Technologies Conference, Keystone, CO, 21–24 August 2006. Reston, VA: AIAA.
19.
CummingsML. The need for command and control instant message adaptive interfaces: lessons learned from tactical tomahawk human-in-the-loop simulations. CyberPsychol Behav2004; 7: 653–661.
ShahAWoolfP. Python environment for bayesian learning: Inferring the structure of bayesian networks from knowledge and data. J Mach Learn Res2009; 10: 159–162.
