Towards self-organizing Kalman filters

Distributed Kalman filtering is an important signal processing method for state estimation in large-scale sensor networks. However, existing solutions do not account for unforeseen events that are likely to occur and thus dramatically changing the operational conditions (e.g. node failure, communication deterioration). This article presents an integration solution for distributed Kalman filtering with distributed self-organization to cope with these events. An overview of existing methods on both topics is presented, followed by an empirical case study of a self-organizing sensor network for observing the contaminant distribution process across a large area in time.