Title :
Towards self-organizing Kalman filters
Author :
Sijs, Joris ; Papp, Zoltan
Author_Institution :
TNO Tech. Sci., The Hague, Netherlands
Abstract :
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.
Keywords :
Kalman filters; state estimation; contaminant distribution process; distributed Kalman filtering; large-scale sensor networks; self-organizing Kalman filters; self-organizing sensor network; signal processing method; state estimation; Computational modeling; Kalman filters; Matrix decomposition; Nickel; State estimation;
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2