  We propose a novel deep neural network (DNN) based approximation architecture
to learn estimates of measurements. We detail an algorithm that enables
training of the DNN. The DNN estimator only uses measurements, if and when they
are received over a communication network. The measurements are communicated
over a network as packets, at a rate unknown to the estimator. Packets may
suffer drops and need retransmission. They may suffer waiting delays as they
traverse a network path.
  Works on estimation often assume knowledge of the dynamic model of the
measured system, which may not be available in practice. The DNN estimator
doesn't assume knowledge of the dynamic system model or the communication
network. It doesn't require a history of measurements, often used by other
works.
  The DNN estimator results in significantly smaller average estimation error
than the commonly used Time-varying Kalman Filter and the Unscented Kalman
Filter, in simulations of linear and nonlinear dynamic systems. The DNN need
not be trained separately for different communications network settings. It is
robust to errors in estimation of network delays that occur due to imperfect
time synchronization between the measurement source and the estimator. Last but
not the least, our simulations shed light on the rate of updates that result in
low estimation error.
