Distributed Information Fusion in Communications Networks with Ad Hoc Connectivity and Non-Deterministic Link Characteristics

Abstract

This thesis establishes algorithms and analytical methods for implementing and evaluating distributed information fusion in networks with non-deterministic communications connectivity. The methods developed in this thesis encompass any sequence of fusion events within an arbitrary network resulting from random channel characteristics, network delays, and ad hoc networking. A distributed fusion approach is developed and proposed as a general solution for distributed fusion agents, enabling each agent to operate autonomously and collaboratively as network conditions allow. The resulting Local Fusion Graph method enables fusion agents to exchange data on an ad hoc or opportunistic basis for distributed fusion. The method’s decentralized approach is inherently able to overcome difficulties experienced by existing distributed fusion methods resulting from messages that are dropped, delayed, or received out of order. The method provides an algorithm that can be implemented into distributed fusion agents without a priori knowledge of network architecture, membership, or communications patterns. It also provides a graphical approach that can be used for analysis as well as simulation model development. A stochastic-based fusion formulation is similarly developed and proposed as a general solution for distributed estimation and trend analysis. The method encapsulates the effects of non-deterministic behaviors and characteristics into probabilistic factors that are integrated into the fusion equations. The resulting stochastic fusion method enables average estimation performance of distributed fusion networks having non-deterministic characteristics and ad hoc connectivity. The method also greatly simplifies the simulation and analysis performance approximations for distributed fusion by providing significant reductions in simulation complexity and computational requirements while using non-idealized communications characteristics. The two methods are implemented in computer-based models for distributed tracking in networks with non-deterministic connectivity. The results of the computer models are used to assess general trends in estimation capabilities with respect to average network connectivity. The analyses also address communications requirements relative to other distributed fusion approaches in the context of estimation accuracy. The results of this thesis are the ability to implement, model, and assess distributed information fusion in arbitrary communications networks with realistic communications characterizations. The solutions proposed in this thesis demonstrate increased estimation capabilities under non-ideal networking conditions that are characteristic of wireless mobile networking environments.