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We apply the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with on-board sensors for tracking multiple ground targets. While POMDPs are intractable to optimize exactly, principled approximation methods can be devised based on Bellman's principle. We introduce a new approximation method called nominal belief-state optimization (NBO). We show that NBO, combined with otherdoi:10.1109/acc.2009.5159963 dblp:conf/amcc/MillerHC09 fatcat:s4iccy2yg5bt7kcqbq2agikotq