Welcome to the Data Fusion Lab!

The Data Fusion Lab was founded by Prof. Dr.-Ing. Marcus Baum in 2015. The group investigates novel methods for sensor data processing and fusion, i.e., signal and image processing, state estimation, and machine learning. A special focus of the group are tracking problems, i.e., the successive localization of one or more mobile objects. A typical application is environment perception of autonomous systems such as intelligent vehicles.


  • December 1, 2023:

    Aaron Kurda joins the Data Fusion Lab.

  • November 30, 2023:

    The paper “Single-Frame Radar Odometry Incorporating Bearing Uncertainty” by K. Thormann and M. Baum receives the award for the Best Paper, 2nd Runner Up at the Combined IEEE MFI/SDF conference.

  • May 1, 2023:

    The paper “The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking” by E. Ernst, F. Pfaff, U. D. Hanebeck, and M. Baum is among the five finalists for the Best Student Paper Award of the ACC 2023 conference in San Diego.

  • July 13, 2022:

    PhD defense of Jaya Shradha Fowdur with the title “Multiple Extended Target Tracking in Maritime Environment Using Marine Radar Data”

  • December 10, 2021:

    PhD defense of Hauke Kaulbersch with the title “Automotive Target Models for Point Cloud Sensors”

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Publications: Recent Highlight