Analytical and Computational Evaluation of Scalable Distributed Fusion Algorithms

Abstract

The theoretical fundamentals of distributed information fusion have been developed over the past two decades and are now fairly well established. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system such as ad hoc sensor networks, the communication architecture is not fixed. In those cases, the distributed fusion approaches based on pedigree information may not scale because of limited communication bandwidth. In this paper, we focus on scalable fusion algorithms and conduct analytical performance evaluation to compare their performance. The goal is to understand the performance of these algorithms under different operating conditions. Specifically, we evaluate the performance of channel filter fusion, naive fusion, Chernoff fusion, Shannon fusion, and Bhattacharyya fusion algorithms. We also compare their performance to optimal centralized fusion under a specific communication pattern. The results show that the channel filter fusion, representing a first order approximation to the information graph fusion, is the only consistent fusion algorithm.