We present a framework to search for and track a target within an urban environment by fusing data from an Unmanned Aerial Vehicle and Unattended Ground Sensors. The target and UAV are restricted to a road network modeled as a directed graph with the ground sensors deployed along selected edges. The UAV is equipped with an onboard camera capable of detecting the target, and it is guided by an information-theoretic planner that uses a particle filter estimate of the target state as its input. We introduce a method to process out-of-sequence measurements that exploits the time-sparseness of the UGS readings to reduce the computational complexity. Finally, we present simulation results on real road networks that show the target tracking performance and the gains in computation time of our approach.
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