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  1. This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targets enter or leave, and there are many sources of sensing uncertainty, including false positive detections, false negative detections, and measurement noise. The robots use a novel distributed Multiple Hypothesis Tracker (MHT) to estimate both the number of targets and the states of each target. A key contribution is a new data association method that reallocates target tracks across the team. The distributed MHT is compared against another distributed multi-target tracker to test its utility for multi-robot, multi-target tracking. 
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  2. This paper compares different distributed control approaches that enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have limited field of view (FoV) and are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the future search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods, Particle Swarm Optimization (PSO) and Simulated Annealing (SA). PSO and SA are traditionally used to find a single global maximum, therefore this paper describes novel formulations of PSO and SA to solve the problem of multi-target tracking. These new methods more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach. 
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