A variance reduction technique for identification in dynamic networks

Abstract

With advancing technology, systems are becoming increasingly interconnected and form more complex networks. Additionally, more measurements are available from systems due to cheaper sensors. Hence there is a need for identification methods specifically designed for networks. For dynamic networks with known interconnection structures, several methods have been proposed for obtaining consistent estimates. We suppose that the internal variables in the network are measured with noise, but that there are external reference signals present in the network that are known exactly. A method that is able to deal with this situation is the two stage method, which solves several open loop identification problems sequentially. In this paper it is shown that solving the problems simultaneously leads to estimates with lower variance.