  We study the problem of tracking multiple moving targets using a team of
mobile robots. Each robot has a set of motion primitives to choose from in
order to collectively maximize the number of targets tracked or the total
quality of tracking. Our focus is on scenarios where communication is limited
and the robots have limited time to share information with their neighbors. As
a result, we seek distributed algorithms that can find solutions in bounded
amount of time. We present two algorithms: (1) a greedy algorithm that is
guaranteed finds a $2$-approximation to the optimal (centralized) solution
albeit requiring $|R|$ communication rounds in the worst-case, where $|R|$
denotes the number of robots; and (2) a local algorithm that finds a
$\mathcal{O}\left((1+\epsilon)(1+1/h)\right)$-approximation algorithm in
$\mathcal{O}(h\log 1/\epsilon)$ communication rounds. Here, $h$ and $\epsilon$
are parameters that allow the user to trade-off the solution quality with
communication time. In addition to theoretical results, we present empirical
evaluation including comparisons with centralized optimal solutions.
