Date of Completion

4-29-2018

Embargo Period

4-29-2018

Keywords

Context-aware decision support, context representation, uncertainty management, proactive decision support, auction algorithm, evolutionary algorithm (EA), hidden Markov model (HMM), Voronoi tessellation, multi-objective UAV path planning, dynamic resource management, asset allocation.

Major Advisor

Krishna R. Pattipati

Associate Advisor

Yaakov Bar-Shalom

Associate Advisor

Peter B. Luh

Field of Study

Electrical Engineering

Degree

Doctor of Philosophy

Open Access

Campus Access

Abstract

Future maritime battlespace environments are expected to be complex, distributed and network centric. Given our ability to collect massive amounts of data using heterogeneous sources, it is imperative to process the collected data and present relevant information to the decision makers (DMs) in a timely manner to make effective decisions under dynamic, uncertain and unpredictable mission scenarios (e.g., sudden changes in mission goals, environment, assets and mission tasks). In addition to data/information processing, it is crucial to dynamically allocate and route the scarce and expensive resources to maximize the amount of decision-relevant information collected, while increasing the probability of mission success. These challenges necessitate the development of a framework for the acquisition, fusion, and transfer of the right data/information/knowledge from the right sources in the right context to the right DM at the right time for the right purpose - a concept known as 6R. The research in this thesis seeks to develop and test algorithms for proactive decision making to extract, process, and integrate relevant information and improve dynamic resource management for mission planning and re-planning. We envision a proactive decision support framework for C4ISR (Command Control Communications Computers, Intelligence, Surveillance and Reconnaissance) planning process that dynamically invokes plans as a function of emerging events, readily adapts plans to meet unfolding events, monitors the outcomes of many of its previous decisions, and re-plans, if warranted, within the context of: 1) Antisubmarine Warfare (ASW) mission planning and generalized bounds on probability of target detection; 2) Multi-objective coordinated path planning for Unmanned Aerial Vehicles (UAVs); 3) Dynamic resource management for maritime mission planning; and 4) Distributed command and control architectures for efficient communication and workload balance in a network.

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