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This textbook presents the theory of Kalman filtering in an easy-to-understand way. The authors provide an introduction to Kalman filters and their application in embedded systems. In addition, the design of Kalman filters is demonstrated using concrete practical examples – individual steps are explained in detail throughout the book.
Kalman filters are the method of choice for eliminating interference signals from sensor data. This is particularly important because many technical systems obtain their process-relevant information via sensors. However, every sensor measurement contains errors due to various factors. If a system were to operate solely based on these inaccurate sensor readings, many applications—such as navigation systems or autonomous systems—would not be feasible.
The book is suitable for interested bachelor's and master's students in the fields of computer science, mechanical engineering, electrical engineering, and mechatronics. It is also a valuable resource for engineers and researchers who want to use a Kalman filter, for example, for data fusion or the estimation of unknown variables in real-time applications.
This textbook presents the theory of Kalman filtering in an easy-to-understand way. The authors provide an introduction to Kalman filters and their application in embedded systems. In addition, the design of Kalman filters is demonstrated using concrete practical examples – individual steps are explained in detail throughout the book.
Kalman filters are the method of choice for eliminating interference signals from sensor data. This is particularly important because many technical systems obtain their process-relevant information via sensors. However, every sensor measurement contains errors due to various factors. If a system were to operate solely based on these inaccurate sensor readings, many applications—such as navigation systems or autonomous systems—would not be feasible.
The book is suitable for interested bachelor's and master's students in the fields of computer science, mechanical engineering, electrical engineering, and mechatronics. It is also a valuable resource for engineers and researchers who want to use a Kalman filter, for example, for data fusion or the estimation of unknown variables in real-time applications.
Prof. Dr. Reiner Marchthaler holds a professorship in the field of "Embedded Systems" in the Faculty of Computer Science and Engineering at Esslingen University of Applied Sciences, specializing in data fusion.
Sebastian Dingler studied Computer Engineering and Computer Science at Esslingen University of Applied Sciences and at the Karlsruhe Institute of Technology (KIT).
Introductory Example.-State Space Representation.- Probability Theory.- Signal Theory.- Classical Kalman Filter.- Adaptive Kalman Filter (ROSE Filter).- Nonlinear Kalman Filters.- System Noise.- Quality Measures.- General Procedure.- Example: Bias Estimation.- Example: Kinematic Models. - Example: Measurement Noise with Offset.- Example: Alternative Motion Model of the Lunar Module.- Example: Covariance Matrix of Measurement Noise.- Example: Environmental Sensor with ROSE Filter.- Example: Lane Detection.- Example: DC Motor.- Example: Position and Velocity Estimation with EKF Filter.
| Erscheinungsjahr: | 2026 |
|---|---|
| Fachbereich: | Nachrichtentechnik |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xix
235 S. 89 s/w Illustr. 82 farbige Illustr. 235 p. 171 illus. 82 illus. in color. |
| ISBN-13: | 9783658503871 |
| ISBN-10: | 3658503874 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Marchthaler, Reiner
Dingler, Sebastian |
| Hersteller: |
Springer
Springer Fachmedien Wiesbaden Springer Fachmedien Wiesbaden GmbH |
| Verantwortliche Person für die EU: | Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, D-65189 Wiesbaden, juergen.hartmann@springer.com |
| Maße: | 240 x 168 x 14 mm |
| Von/Mit: | Reiner Marchthaler (u. a.) |
| Erscheinungsdatum: | 24.02.2026 |
| Gewicht: | 0,487 kg |
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