An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter
The increasing trend towards connected sensors (“internet of things” and “ubiquitous computing”) derive a demand for powerful distributed estimation methodologies. In tracking applications, the “Distributed Kalman Filter” (DKF) provides an optimal solution under certain conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm which receives either all associated measurements or so-called “tracklets”. However, this scheme needs the result of each update step for the optimal solution whereas the DKF works at arbitrary communication rates since the calculation is completely distributed. Two more recent methodologies are based on the “Accumulated State Densities” (ASD) which augment the states from multiple time instants. In practical applications, tracklet fusion based on the equivalent measurement often achieves reliable results even if full communication is not available. The limitations and robustness of the tracklet fusion will be discussed.
At first, the Distinguished Lecture will explain the origin of the challenges in distributed tracking. Then, possible solutions to them are derived and illuminated. In particular, algorithms will be provided for each presented solution.
The list of topics includes: Short introduction to target tracking, Tracklet Fusion, Exact Fusion with cross-covariances, Naive Fusion, Federated Fusion, Decentralized Fusion (Consensus Kalman Filter), Distributed Kalman Filter (DKF), Debiasing for the DKF, Distributed ASD Fusion, Augmented State Tracklet Fusion.
Date and Time
Location
Hosts
Registration
- Date: 04 Apr 2025
- Time: 08:00 AM UTC to 10:00 AM UTC
-
Add Event to Calendar
- Nowowiejska 15/19
- Warsaw University of Technology
- Warsaw, Mazowieckie
- Poland 00-665
- Building: Faculty of Electronics and Information Technology
- Room Number: 229
Speakers
Felix Govaers of Fraunhofer Institute for Communication
An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter
Biography:
Felix Govaers received his Diploma in Mathematics and did 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 and Information Fusion (SDF) where he was leading the research group “Distributed Systems” from 2014 to 2017. Currently he is the deputy head of the department, where he manages research proposals with industry partners and public calls, he does the scientific foresight for strategic decisions and basic research projects. He also represents the institute in technical discussions and presentations for the Bundeswehr, NATO, and public events. He regularly delivers lectures on data fusion and object tracking in distributed systems at the University of Bonn since 2011. As a technical supervisor of numerous theses for Bachelor, Master, and PhD, he is collaborating with younger researchers and spreading ideas and methodologies. The research of Felix Govaers (h-index 10) is focused on data fusion for state estimation in non- linear scenarios and in sensor networks. This includes track-extraction, processing of delayed measurements as well as the Distributed Kalman filter and track-to-track fusion. Current research projects develop innovative algorithms based on tensor decompositions for discrete density representations in multi target tracking. He is also interested in advances in state estimation such as particle flow and homotopy filters, extended target tracking, and the random finite set theory approaches. Felix Govaers regularly provides a tutorial on distributed data fusion at the international FUSION conference since many years. He serves as the treasurer for the Germany Section of the IEEE Aerospace and Electronic Systems Society since 2015 and as an Associate Editor for the Transactions of the AES since 2014. He organizes the symposium “Sensor Data Fusion: Trends, Solutions, Applications” as the Technical Program Chair on a yearly basis and has served as a Program Chair for the FUSION conference.