On the Control and Estimation Over Relative Sensing Networks

In this note, we consider certain structural aspects of estimation and control over relative sensing networks (RSNs). In this venue, using tools from basic algebraic graph theory-namely, the incidence matrix and cut and cycle spaces of a connect graph- we examine transformations among relative sensing topologies for a networked system. These transformations are parameterized for noise-free as well as noisy networks and their utility in the context of network-centric robust control and control reconfigurations is explored.