This paper considers a sequential estimation and sensor scheduling problem
with one sensor and one estimator. The sensor makes sequential observations
about the state of an underlying memoryless stochastic process, and makes a
decision as to whether or not to send this measurement to the estimator. The
sensor and the estimator have the common objective of minimizing expected
distortion in the estimation of the state of the process, over a finite time
horizon, with the constraint that the sensor can transmit its observation only
a limited number of times. As opposed to the prior work where communication
between the sensor and the estimator was assumed to be perfect (noiseless), in
this work an additive noise channel with fixed power constraint is considered;
hence, the sensor has to encode its message before transmission. For some
specific source and channel noise densities, we obtain the optimal encoding and
estimation policies in conjunction with the optimal transmission schedule. The
impact of the presence of a noisy channel is analyzed numerically based on
dynamic programming. This analysis yields some rather surprising results such
as a phase-transition phenomenon in the number of used transmission
opportunities, which was not encountered in the noiseless communication
setting.Comment: X. Gao, E. Akyol, and T. Basar. Optimal estimation with limited
measurements and noisy communication. In 54th IEEE Conference on Decision and
Control (CDC15), 2015, to appea