  We study two sensor assignment problems for multi-target tracking with the
goal of improving the observability of the underlying estimator. In the
restricted version of the problem, we focus on assigning unique pairs of
sensors to each target. We present a 1/3-approximation algorithm for this
problem. We use the inverse of the condition number as the value function. If
the target's motion model is not known, the inverse cannot be computed exactly.
Instead, we present a lower bound for range-only sensing.
  In the general version, the sensors must form teams to track individual
targets. We do not force any specific constraints on the size of each team,
instead assume that the value function is monotonically increasing and is
submodular. A greedy algorithm that yields a 1/2-approximation. However, we
show that the inverse of the condition number is neither monotone nor
submodular. Instead, we present other measures that are monotone and
submodular. In addition to theoretical results, we evaluate our results
empirically through simulations.
