  A state estimator is derived for an agent with the ability to measure single
ranges to fixed points in its environment, and equipped with an accelerometer
and a rate gyroscope. The state estimator makes no agent-specific assumptions,
and can be immediately applied to any rigid body with these sensors. Also, the
state estimator doesn't use any trilateration-based method to calculate
position from range measurements. As the considered system can only make a
single range measurement at a time, we present a greedy optimization algorithm
for selecting the best measurement. Experiments in an indoor testbed using an
externally controlled multicopter demonstrate the efficacy of the algorithm,
specifically showing an improvement over a na\"ive strategy of a fixed sequence
of measurements. In separate experiments, the algorithm is also used in
feedback control, to control the position of the multicopter.
