ABSTRACT
This chapter focuses on the estimation problem where the goal of fusion is to compute an estimate of the state from measurements collected by multiple sensors. It presents the fundamental concepts for distributed estimation, which are crucial for developing distributed fusion algorithms. The chapter discusses various distributed fusion architectures, their advantages and disadvantages, the use of information graph to represent information flow, and selection of an appropriate architecture. It explains the Bayesian fusion equation for combining two probability functions, or their means and covariances. The chapter shows how the information graph can be used to keep track of information flow in a distributed estimation system and how it can be used to derive fusion equations for various fusion architectures. It examines some suboptimal but practical approaches that are based on approximations of the optimal approach. The optimal fusion algorithm for arbitrary distributed fusion architectures is found by repeated application of the Bayesian fusion equation.
