Optimization of measurement schedules and sensor designs for linear dynamic systems

This paper presents new results on the problem of measurement scheduling, sensor location, and design for linear dynamic systems. Both time-invariant and time-varying systems are considered and different norms of the observability and information matrices are maximized with respect to the structural parameters of the system. A close connection is established between these problems and the Kiefer-Wolfowitz theory of experimental design for regression problems. Both randomized and nonrandomized designs are considered. It is shown that the optimal designs obey certain minmax properties that lead to rapidly convergent algorithms. The results are illustrated by an analytical and a numerical example.