Distributed and Adaptive Target Tracking with a Sensor Network

Ensuring the robustness and resilience of safety-critical systems from civil aviation to military surveillance technologies requires improvements to target tracking capabilities. Implementing target tracking as a distributed function can improve the quality and availability of information for end users. Any errors in the model of a target's dynamics or a sensor network's measurement process will result in estimates with degraded accuracy or even filter divergence. This dissertation solves a distributed estimation problem for estimating the state of a dynamical system and the parameters defining a model of that system.

The novelty of this work lies in the ability of a sensor network to maintain consensus on state and parameter estimates through local communications between sensor platforms.