Robust Optimal Sensor Selection Using Information Theoretic Measures

In this paper, we address the problem of optimal sensor selection to ensure estimability in linear, steady-state processes in case of sensor failures. To this end, we follow the data reconciliation framework to formulate the sensor selection problem using information theoretic measures such as entropy, forward Kullback-Leibler (KL), reverse KL, and symmetric KL divergences. With the help of convex optimization theory, it was shown that all the proposed formulations can be solved to global optimality using a branch and bound method. The process flow network is used to illustrate the efficacy of the proposed formulations.