  This paper considers the problem of localizing a set of nodes in a wireless
sensor network when both their positions and the parameters of the
communication model are unknown. We assume that a single agent moves through
the environment, taking measurements of the Received Signal Strength (RSS), and
seek a controller that optimizes a performance metric based on the Fisher
Information Matrix (FIM). We develop a receding horizon (RH) approach that
alternates between estimating the parameter values (using a maximum likelihood
estimator) and determining where to move so as to maximally inform the
estimation problem. The receding horizon controller solves a multi-stage look
ahead problem to determine the next control to be applied, executes the move,
collects the next measurement, and then re-estimates the parameters before
repeating the sequence. We consider both a Dynamic Programming (DP) approach to
solving the optimal control problem at each step, and a simplified heuristic
based on a pruning algorithm that significantly reduces the computational
complexity. We also consider a modified cost function that seeks to balance the
information acquired about each of the parameters to ensure the controller does
not focus on a single value in its optimization. These approaches are compared
against two baselines, one based on a purely random trajectory and one on a
greedy control solution. The simulations indicate our RH schemes outperform the
baselines, while the pruning algorithm produces significant reductions in
computation time with little effect on overall performance.
