  We consider two nonlinear state estimation problems in a setting where an
extended Kalman filter receives measurements from two sets of sensors via two
channels (2C). In the stochastic-2C problem, the channels drop measurements
stochastically, whereas in 2C scheduling, the estimator chooses when to read
each channel. In the first problem, we generalize linear-case 2C analysis to
obtain -- for a given pair of channel arrival rates -- boundedness conditions
for the trace of the error covariance, as well as a worst-case upper bound. For
scheduling, an optimization problem is solved to find arrival rates that
balance low channel usage with low trace bounds, and channels are read
deterministically with the expected periods corresponding to these arrival
rates. We validate both solutions in simulations for linear and nonlinear
dynamics; as well as in a real experiment with an underwater robot whose
position is being intermittently found in a UAV camera image.
