Felix Govaers

Fraunhofer Institute for Communication, Information Processing and Ergonomics Germany

Felix Govaers received his Diploma in Mathematics and his PHD with the title 'Advanced Data Fusion in Distributed Sensor Applications” in Computer Science, both at the University of Bonn, Germany. Since 2009 he works at Fraunhofer FKIE in the department for Sensor Data Fusion and Information Processing where he now is the deputy head of the department Sensor Data and Information Fusion (SDF). The research of Felix Govaers is focused on data fusion for state estimation in sensor networks. This includes track-extraction, processing of delayed measurements as well as the Distributed Kalman filter, non-linear filters, and track-to-track fusion.

1books edited

2chapters authored

Latest work with IntechOpen by Felix Govaers

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.

Go to the book

Introduction and Implementations of the Kalman Filter

Edited by Felix Govaers

Chapters authored

Distributed Kalman Filter

By Felix Govaers

The continuing trend toward connected sensors (“internet of things” and” ubiquitous computing”) drives a demand for powerful distributed estimation methodologies. In tracking applications, the distributed Kalman filter (DKF) provides an optimal solution under Kalman filter conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm, which receives all associated measurements. However, the centralized approach requires full communication of all measurements at each time step, whereas the DKF works at arbitrary communication rates since the calculation is fully distributed. A more recent methodology is based on ”accumulated state density” (ASD), which augments the states from multiple time instants to overcome spatial cross-correlations. This chapter explains the challenges in distributed tracking. Then, possible solutions are derived, which include the DKF and ASD approach.

Part of the book: Kalman Filters

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

By Felix Govaers

Part of the book: Introduction and Implementations of the Kalman Filter

Felix Govaers

Latest work with IntechOpen by Felix Govaers

Introduction and Implementations of the Kalman Filter

Chapters authored

Related collaborators

Kok-Yong Seng

Ying Chen

Jia Guo

Qian Zhuang

Weiping Priscilla Fan

Si Hui Maureen Lee

Junxian Ong

Poh Ling Tan

Yu Li Lydia Law

Kai Wei Jason Lee