Simulation-based examination of the limits of performance for decentralized multi-agent surveillance and tracking of undersea targets

This paper examines the limits of performance for an ensemble of cooperating, mobile sensing agents executing an undersea surveillance mission. The objective of the multi-agent ensemble is to minimize uncertainty concerning the presence and location of targets as the multi-target system evolves over time. Each agent is capable of sensing, communicating with other agents, processing data to infer states of interest (fusion), and deciding on and executing motion commands. Each agent continually executes a perception-action cycle in which it fuses information to determine its best estimate of the multi-target system state and decides on its next (and possibly future) motion action(s) to optimize a criterion related to its entropic state (quantification of information gain or loss). Each agent's perception of the states of interest is derived from measurements captured by its own sensor(s) and information communicated by other agents. Each agent's decisions are based on its estimates of the multi target system state, its entropic state, and its predictions of peer agent actions. The multi-agent cooperative decision making can be modeled as a cyclic optimization whereby the joint decision vector is optimized by sequentially optimizing each individual agent's decision vector while holding the others fixed. Moreover, the problem is a cyclic stochastic optimization (CSO) whereby only noisy measurements of the objective function are available to each agent. Preliminary theoretical results have recently emerged regarding convergence conditions and sub-optimality for CSO. This paper examines the implications and applicability of CSO convergence and sub-optimality via simulation- based experiments in the context of a cooperating multi-agent ensemble of undersea sensing agents searching a region for new targets and maintaining track on all discovered targets. Simulation results indicate that the theoretical results provide useful guidance on predicting the empirically observed limits of performance of the multi-agent ensemble.