Sensor placement minimizing the state estimation mean square error: Performance guarantees of greedy solutions

This paper studies selecting a subset of the system's output so that the state estimation mean square error (MSE) is minimized. This results in the maximization problem of a set function defined on possible sensor selections subject to a cardinality constraint. We consider to solve it approximately by greedy search. Since the MSE function is not submodular nor supermodular, the well-known performance guarantees for the greedy solutions do not hold in the present case. We thus introduce the quantities---the submodularity ratio and the curvature---to evaluate the degrees of submodularity and supermodularity of the non-submodular function. By using the properties of the MSE function, we approximately compute these quantities and derive a performance guarantee for the greedy solutions. It is shown that the guarantee is less conservative than those in the existing results.