Hidden Markov Model and Auction-Based Formulations of Sensor Coordination Mechanisms in Dynamic Task Environments

In this paper, multistage auction-based intelligence, surveillance, and reconnaissance (ISR) sensor coordination mechanisms are investigated in the context of dynamic and uncertain mission environments such as those faced by expeditionary strike groups. Each attribute of the mission task is modeled using a hidden Markov model (HMM) with controllable emission matrices, corresponding to each ISR asset package (subset of sensors). For each HMM-asset package pair, we evaluate a matrix of information gains (uncertainty reduction measures). The elements of this matrix depend on the asset coordination structure and the concomitant delays accrued. We consider three coordination structures (distributed ISR coordination, ISR officer serving as a coordinator, and ISR officer serving as a commander) here. We evaluate these structures on a hypothetical mission scenario that requires the monitoring of ISR activities in multiple geographic regions. The three structures are evaluated by comparing the task state estimation error cost, as well as travel, waiting, and assignment delays. The results of the analysis were used as a guide in the design of a mission scenario and asset composition for a team-in-the-loop experimentation. Our solution has the potential to be a mixed initiative decision support tool to an ISR coordinator/commander, where the human provides possible ISR asset package-task pairings and the tool evaluates the efficacy of the assignment in terms of task accuracy and delays. We also apply our approach to a hypothetical disaster management scenario involving chemical contamination and discuss the computational complexity of our approach.