Advances in asynchronous and decentralized estimation

Abstract

Two key challenges associated with fusion of information in large-scale systems are the asynchronous nature of information flow and the consistency requirements associated with decentralized processing. This paper provides contributions in both these areas. First, we build on an existing minimum variance estimation algorithm for out-of-sequence processing of sensor measurements, extending the algorithm to handle multiple lags and multiple dynamic models. We study the performance of the algorithms with numerical examples. Second, we establish a connection between the maximum entropy of a partially known multivariable Gaussian distribution and a particular Bayesian network, whose structure is based on the available information. The connection leads to a useful methodology for identifying missing information in systems described by Bayesian networks, a key tool in developing algorithms for information flow in decentralized systems.