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Automated decision support for placing terrain observers
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatiserat beslutsstöd för placering av terrängobservatörer (Swedish)
Abstract [en]

Access to reliable information is key for military decision-making. Reconnaissance assets are used to gather information about the Operational Environment. These assets need to be placed within the terrain so that they can see as much of the area of interest as possible. The manual task of placing assets within the terrain is a time-consuming task. This thesis examines the design for a system that could be used to generate candidate placement positions to aid the decision-maker. The system’s task is to find positions that maximize visual cover, while keeping the assets as safe as possible. The problem was formalized and reformulated into a multi-objective optimization problem. Three different optimization algorithms were evaluated: Simulated annealing, Tabu Search and the genetic algorithm NSGA-II. The optimization algorithms were tested in three different scenarios to reduce bias. The evaluation showed that NSGA-II had consistent gains over the other two algorithms.

Abstract [sv]

Militär beslutsfattning kräver pålitlig information rörande den operationella miljön. Ett sätt att inhämta information är genom rekognosering. För att rekognoseringsförband ska kunna utföra sitt arbete krävs det att de blir placerade så att de kan övervaka så stor del som möjligt av intresseområdet. Det manuella sättet att hitta och jämföra placeringspunkter är svårt och tidskrävande. Detta examensarbete utforskar utformningen av ett automatiserat system för att stötta beslutsfattaren. Systemet genererar förslag av lämpliga placeringspunkter som kan presenteras till beslutsfattaren. Dessa placeringspunkter är punkter som maximerar det som observatören ser, utan att försämra observatörens säkerhet. Problemet formaliserades och formulerades som ett optimeringsproblem med flera mål. Tre olika algoritmer implementerades och testades mot varandra. Algoritmerna testades i tre olika scenarion som skapades för att minska risken av partiska resultat.

Place, publisher, year, edition, pages
2019. , p. 55
Series
TRITA-EECS-EX ; 2019:610
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264487OAI: oai:DiVA.org:kth-264487DiVA, id: diva2:1373789
External cooperation
Carmenta AB
Educational program
Master of Science in Engineering - Information and Communication Technology
Supervisors
Examiners
Available from: 2019-11-28 Created: 2019-11-28

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