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  1. We study two multi-robot assignment problems for multi-target tracking. We consider distributed approaches in order to deal with limited sensing and communication ranges. We seek to simultaneously assign trajectories and targets to the robots. Our focus is on \emph{local} algorithms that achieve performance close to the optimal algorithms with limited communication. We show how to use a local algorithm that guarantees a bounded approximate solution within $\mathcal{O}(h\log{1/\epsilon})$ communication rounds. We compare with a greedy approach that achieves a $2$--approximation in as many rounds as the number of robots. Simulation results show that the local algorithm is an effective solution to the assignment problem. 
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  2. We study the problem of searching and tracking a collection of moving targets using a robot with a limited Field-of-View (FoV) sensor. The actual number of targets present in the environment is not known a priori. We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for only tracking problems to allow for simultaneous search and tracking. The proposed framework can extract individual target tracks as well as estimate the number and spatial density of the targets. We also show how to use Gaussian Process (GP) regression to extract and predict non-linear target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations where we also compare the performance of two active control strategies. 
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