Sensor scheduling via compressed sensing

We present a novel approach for sensor scheduling which is, in general, a NP-hard problem involving the selection of S out of N sensors such that an optimal filtering performance is attained. Our approach utilizes a heuristic measure that quantifies the incoherence of the vector space defined by the sensors with respect to the system principal directions. This in turn facilitates the formulation of a convex relaxation that can be efficiently solved using a myriad of compressed sensing algorithms.