<title>Fully connected decentralized estimation</title>

Abstract

This paper addresses the multisensor estimation problem for both linear and nonlinear systems in a fully connected decentralized sensing architecture. The sensor data fusion problem is identified and the case for decentralized architectures, rather than hierarchical or centralized ones, is made. Fully connected decentralized estimation algorithms in both state and information spaces are then developed. The intent is to show that decentralized estimation is feasible and to demonstrate the advantages of information space over state space. The decentralization procedure is then repeated for the extended Kalman filter and extended information filter to produce decentralized filters for nonlinear systems. The four filters are compared and contrasted. In appraising the algorithms the problems associated with the requirement for a fully connected topology are identified.