Self organizing distributed state estimators

J. Sijs, Z. Papp

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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Abstract

Distributed solutions for signal processing techniques are important for establishing large-scale monitoring and control applications. They enable the deployment of scalable sensor networks for particular application areas. Typically, such networks consists of a large number of vulnerable components connected via unreliable communication links and are sometimes deployed in harsh environment. Therefore, dependability of sensor network is a challenging problem. An efficient and cost effective answer to this challenge is provided by employing runtime reconfiguration techniques that assure the integrity of the desired signal processing functionalities. Runtime reconfigurability has thorough impact both on system design, implementation, testing/validation and deployment. The presented research focuses on the widespreaded signal processing method known as state estimation with Kalman filtering in particular. To that extent, a number of distributed state estimation solutions that are suitable for networked systems in general are overviewed, after which robustness of the system is improved according to various runtime reconfiguration techniques.
Original languageEnglish
Title of host publicationIntelligent sensor networks : the integration of sensor networks, signal processing and machine learning
EditorsF. Hu, Q. Hao
PublisherCRC Press
Pages484-510
Number of pages674
ISBN (Print)9781439892817
DOIs
Publication statusPublished - 2012

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    Sijs, J., & Papp, Z. (2012). Self organizing distributed state estimators. In F. Hu, & Q. Hao (Eds.), Intelligent sensor networks : the integration of sensor networks, signal processing and machine learning (pp. 484-510). CRC Press. https://doi.org/10.1201/b14300-26