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This paper describes an experimental demonstration of a distributed, decentralized, low communication sensor management algorithm. We first review the mathematics surrounding the method, which includes a novel combination of particle filtering for predictive density estimation and information theory for maximizing information flow. Earlier work has shown the utility via Monte Carlo simulations. Here we present a laboratory demonstration to illustrate the utility and to provide a stepping stone toward full-up implementation. To that end, we describe an inverted Unmanned Aerial Vehicle (UAV) test-bed developed by The General Dynamics Advanced Information Systems (GDAIS) Michigan Research and Development Center (MRDC) to facilitate and promote the maturation of the research algorithm into an operational, field-able system. Using a modular design with wheeled robots as surrogates to UAVs, we illustrate how the method is able to detect and track moving targets over a large surveillance region by tasking a collection of limited field of view sensors.

This paper describes a decentralized low communication approach to multi-platform sensor management. The method is based on a physicomimetic relaxation to a joint information theoretic optimization, which inherits the benefits of information theoretic scheduling while maintaining tractability. The method uses only limited message passing, only neighboring nodes communicate, and each node makes its own sensor management decisions.We show by simulation that the method allows a network of sensor nodes to automatically self organize and perform a global task. In the model problem, a group of unmanned aerial vehicles (UAVs) hover above a ground surveillance region. An initially unknown number of moving ground targets inhabit the region. Each UAV is capable of making noisy measurements of the patch of ground directly below, which provide evidence as to the presence or absence of targets in that sub-region. The goal of the network is to determine the number of targets and their individual states (positions and velocities) in the entire surveillance region through repeated interrogation by the individual nodes. As the individual nodes can only see a small portion of the ground, they must move in a manner that is both responsive to measurements and coordinated with other nodes.

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