Open access peer-reviewed Edited Volume

Introduction and Implementations of the Kalman Filter

Edited by Felix Govaers

Fraunhofer Institute for Communication, Information Processing and Ergonomics, Germany

Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.

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Introduction and Implementations of the Kalman FilterEdited by Felix Govaers

Published: May 22nd 2019

DOI: 10.5772/intechopen.75731

ISBN: 978-1-83880-537-1

Print ISBN: 978-1-83880-536-4

eBook (PDF) ISBN: 978-1-83880-739-9

Copyright year: 2019

Books open for chapter submissions

13109 Total Chapter Downloads

12 Crossref Citations

2 Web of Science Citations

25 Dimensions Citations


Open access peer-reviewed

1. Introductory Chapter: Kalman Filter - The Working Horse of Object Tracking Systems Nowadays

By Felix Govaers


Open access peer-reviewed

2. Introduction to Kalman Filter and Its Applications

By Youngjoo Kim and Hyochoong Bang


Open access peer-reviewed

3. Tuning of the Kalman Filter Using Constant Gains

By Mudambi R. Ananthasayanam


Open access peer-reviewed

4. Statically Fused Converted Measurement Kalman Filters

By Gongjian Zhou and Zhengkun Guo


Open access peer-reviewed

5. A Scalable, FPGA-Based Implementation of the Unscented Kalman Filter

By Jeremy Soh and Xiaofeng Wu


Open access peer-reviewed

6. Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model

By Rajamani Doraiswami and Lahouari Cheded


Edited Volume and chapters are indexed in

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