Keywords:
Kalman filter
Description / Table of Contents:
Chapter 1: A Reference Recursive Recipe for Tuning the Statistics of the Kalman Filter by Mudambi R Ananthasayanam ---
Chapter 2: The Error Covariance Matrix Inflation in Ensemble Kalman Filter by Guocan Wu and Xiaogu Zheng ---
Chapter 3: Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics by Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias Ortmaier ---
Chapter 4: Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems by Mauro Hernán Riva, Mark Wielitzka and Tobias Ortmaier ---
Chapter 5: Kalman Filters for Parameter Estimation of Nonstationary Signals by Sarita Nanda ---
Chapter 6: Kalman Filter Models for the Prediction of Individualised Thermal Work Strain by Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee, Junxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and Kok-Yong Seng ---
Chapter 7: Application of Kalman Filtering in Dynamic Prediction for Corporate Financial Distress by Qian Zhuang ---
Chapter 8: Predicting Collisions in Mobile Robot Navigation by Kalman Filter by Angel Sánchez, Homero Ríos, Gustavo Quintana and Antonio Marín ---
Chapter 9: Efficient Matrix-Free Ensemble Kalman Filter Implementations: Accounting for Localization by Elias David Niño Ruiz, Rolando Beltrán Arrieta and Alfonso Manuel Mancilla Herrera ---
Chapter 10: Kalman Filters for Reference Current Generation in Shunt Active Power Filter (APF) by Ahmad Shukri Bin Abu Hasim, Syed Mohd Fairuz Bin Syed Mohd Dardin and Zulkifilie Bin Ibrahim ---
Chapter 11: Applications of Kalman Filters for Coherent Optical Communication Systems by Lalitha Pakala and Bernhard Schmauss ---
Chapter 12: Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design by Kenshi Saho ---
Chapter 13: Distributed Kalman Filter by Felix Govaers ---
Chapter 14: Consensus-Based Distributed Filtering for GNSS by Amir Khodabandeh, Peter J.G. Teunissen and Safoora Zaminpardaz
Pages:
Online-Ressource (314 Seiten)
ISBN:
9789535138280
URL:
https://www.intechopen.com/books/kalman-filters-theory-for-advanced-applications
Language:
English
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