  We consider a scenario in which a group of quadrotors is tasked at tracking
multiple stationary targets in an unknown, bounded environment. The quadrotors
search for targets along a spatial grid overlaid on the environment while
performing a random walk on this grid modeled by a discrete-time discrete-state
(DTDS) Markov chain. The quadrotors can transmit their estimates of the target
locations to other quadrotors that occupy their current location on the grid;
thus, their communication network is time-varying and not necessarily
connected. We model the search procedure as a renewal-reward process on the
underlying DTDS Markov chain. To accommodate changes in the set of targets
observed by each quadrotor as it explores the environment, along with
uncertainties in the quadrotors' measurements of the targets, we formulate the
tracking problem in terms of Random Finite Sets (RFS). The quadrotors use
RFS-based Probability Hypothesis Density (PHD) filters to estimate the number
of targets and their locations. We present a theoretical estimation framework,
based on the Gaussian Mixture formulation of the PHD filter, and preliminary
simulation results toward extending existing approaches for RFS-based
multi-target tracking to a decentralized multi-robot strategy for multi-target
tracking. We validate this approach with simulations of multi-target tracking
scenarios with different densities of robots and targets, and we evaluate the
average time required for the robots in each scenario to reach agreement on a
common set of targets.
