Distributed Estimation and Control | in Stochastic Communication Networks

Nilan Marktanner

ISBN 10: 3639726596 / ISBN 13: 9783639726596

Published by AV Akademikerverlag, 2014

About this Item

Distributed Estimation and Control | in Stochastic Communication Networks | Nilan Marktanner | Taschenbuch | 64 S. | Englisch | 2014 | AV Akademikerverlag | EAN 9783639726596 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 104965472

Bibliographic Details

• Title: Distributed Estimation and Control | in Stochastic Communication Networks
• Publisher: AV Akademikerverlag
• Publication Date: 2014
• Language: English
• ISBN 10: 3639726596
• ISBN 13: 9783639726596
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 113 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

In recent years, the popularity for wireless network setups has risen as they provide flexible communication. Therefore, there is an increasing interest in methods to counteract the drawbacks of such communication networks. Handling packet losses and packet delays is particular challenging. Different approaches will be presented to tackle these problems. Central observations that are also useful for the following approaches will be made using the Centralized Kalman Filter for time-varying information, which yields optimal results under certain assumptions. The examination of time-invariant or scalar systems yields further simplifying observations. The distributed counterpart to the Centralized Kalman Filter, the Distributed Kalman Filter for time-varying information, will be derived by decomposing the formulas from the central case without losing the optimality of the estimate. Finally, the Hypothesizing Kalman Filter for time-varying information is able to cope with packet loss without relying on previous assumptions of the former approaches. Depending on hypotheses about the communication structure, the filter is able to provide up to optimal estimates.

About the Author

Nilan completed his Bachelor studies with a major in Computer Science at Karlsruhe Institute of Technology (KIT) in September 2014.His interests lie in estimation and control of dynamical systems, robotics, machine learning, data science.In his freetime, Nilan likes to watch movies with friends, play computer games or listen to music.