about me

I am a postdoctoral researcher in the Intelligent Sensor-Actuator-Systems (ISAS) laboratory at the Karlsruhe Institute of Technology (KIT), Germany. I have obtained my Ph.D. from Karlsruhe Institute of Technology under the supervision of Prof. Uwe D. Hanebeck (KIT) and Dr. Simon J. Julier (UCL, UK). On this website, you can find information about my work and research interests.

from observation to information

My research in the field of state estimation theory includes robust Kalman filtering, distributed and decentralized information processing for sensor networks, and event-based filtering. With state estimation, usable information can be extracted from sensor data — even under severe uncertainties. Quantifying uncertainty in predictions and estimates is essential for the assessment and design of robust filtering techniques. Further important challenges in developing filtering techniques arise from nonlinear process and sensor models and from spatially distributed systems. In view of these challenges, I’m interested in consistent and robust solutions.

research overview

news

  • March 02, 2017 Automatica article “Decentralized Data Fusion with Inverse Covariance Intersection” added.
  • February 27, 2017 Our paper for the 20th IFAC World Congress in Toulouse, France, has been accepted.
  • July 18, 2016 Four papers for the MFI conference in Baden-Baden, Germany, have been accepted.
  • July 01, 2016 GAČR-DFG project “Cooperative Approaches to Design of Nonlinear Filters” with the Department of Computer Science and Engineering at the University of West Bohemia starts today.
  • May 09, 2016 Two papers for the FUSION conference in Heidelberg, Germany, have been accepted.
  • January 01, 2016 BMWi project “Fertigung und Entwicklung von Tanks unter Industrie4.0-Bedingungen” starts today.
  • December 16, 2015 Article for tm - technisches messen (teme) has been accepted.
  • November 24, 2015 Book chapter in Event-Based Control and Signal Processing is out.

recent publications

  1. Event-based State Estimation in a Feedback Loop with Imperfect Communication Links (to appear)

    Joris Sijs and Benjamin Noack

    Proceedings of the 20th IFAC World Congress (IFAC 2017), Toulouse, France

    July, 2017

    @inproceedings{IFAC17_Sijs,
      title = {{Event-based State Estimation in a Feedback Loop with Imperfect Communication Links (to appear)}},
      author = {Sijs, Joris and Noack, Benjamin},
      booktitle = {Proceedings of the 20th IFAC World Congress (IFAC 2017)},
      year = {2017},
      address = {Toulouse, France},
      month = {jul},
    }
    
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  2. Decentralized Data Fusion with Inverse Covariance Intersection

    Benjamin Noack, Joris Sijs, Marc Reinhardt, and Uwe D. Hanebeck

    In Automatica, vol. 79

    May, 2017

    Abstract:

    In distributed and decentralized state estimation systems, fusion methods are employed to systematically combine multiple estimates of the state into a single, more accurate estimate. An often encountered problem in the fusion process relates to unknown common information that is shared by the estimates to be fused and is responsible for correlations. If the correlation structure is unknown to the fusion method, conservative strategies are typically pursued. As such, the parameterization introduced by the ellipsoidal intersection method has been a novel approach to describe unknown correlations, though suitable values for these parameters with proven consistency have not been identified yet. In this article, an extension of ellipsoidal intersection is proposed that guarantees consistent fusion results in the presence of unknown common information. The bound used by the novel approach corresponds to computing an outer ellipsoidal bound on the intersection of inverse covariance ellipsoids. As a major advantage of this inverse covariance intersection method, fusion results prove to be more accurate than those provided by the well-known covariance intersection method.

    @article{Automatica17_Noack,
      title = {{Decentralized Data Fusion with Inverse Covariance Intersection}},
      author = {Noack, Benjamin and Sijs, Joris and Reinhardt, Marc and Hanebeck, Uwe D.},
      journal = {Automatica},
      year = {2017},
      month = {may},
      pages = {35--41},
      volume = {79},
      doi = {10.1016/j.automatica.2017.01.019},
      url = {http://dx.doi.org/10.1016/j.automatica.2017.01.019},
    }
    
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  3. Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information

    Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, and Jürgen Beyerer

    Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany

    March, 2017

    Abstract:

    Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems tailored to a task at hand, for instance by means of sensors and implementations of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motion-related information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.

    @inproceedings{OCM17_Maier,
      title = {{Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information}},
      author = {Maier, Georg and Pfaff, Florian and Becker, Florian and Pieper, Christoph and Gruna, Robin and Noack, Benjamin and Kruggel-Emden, Harald and L{\"{a}}ngle, Thomas and Hanebeck, Uwe D. and Wirtz, Siegmar and Scherer, Viktor and Beyerer, J{\"{u}}rgen},
      booktitle = {Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017)},
      year = {2017},
      address = {Karlsruhe, Germany},
      month = {mar},
      url = {https://www.ksp.kit.edu/9783731506126},
    }
    
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  4. Simulation-based Evaluation of Predictive Tracking for Sorting Bulk Materials

    Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Harald Kruggel-Emden, Robin Gruna, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, and Jürgen Beyerer

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    Multitarget tracking problems arise in many real-world applications. The performance of the utilized algorithm strongly depends both on how the data association problem is handled and on the suitability of the motion models employed. Especially the motion models can be hard to validate. Previously, we have proposed to use multitarget tracking to improve optical belt sorters. In this paper, we evaluate both the suitability of our model and the tracking and then of our entire system incorporating the image processing component via the use of highly realistic numerical simulations. We first assess the model using noise-free measurements generated by the simulation and then evaluate the entire system by using synthetically generated image data.

    @inproceedings{MFI16_Pfaff,
      title = {{Simulation-based Evaluation of Predictive Tracking for Sorting Bulk Materials}},
      author = {Pfaff, Florian and Pieper, Christoph and Maier, Georg and Noack, Benjamin and Kruggel-Emden, Harald and Gruna, Robin and Hanebeck, Uwe D. and Wirtz, Siegmar and Scherer, Viktor and L{\"{a}}ngle, Thomas and Beyerer, J{\"{u}}rgen},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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  5. Algebraic Analysis of Data Fusion with Ellipsoidal Intersection

    Benjamin Noack, Joris Sijs, and Uwe D. Hanebeck

    Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany

    September, 2016

    Abstract:

    For decentralized fusion problems, ellipsoidal intersection has been proposed as an efficient fusion rule that provides less conservative results as compared to the well-know covariance intersection method. Ellipsoidal intersection relies on the computation of a common estimate that is shared by the estimates to be fused. In this paper, an algebraic reformulation of ellipsoidal intersection is discussed that circumvents the computation of the common estimate. It is shown that ellipsoidal intersection corresponds to an internal ellipsoidal approximation of the intersection of covariance ellipsoids. An interesting result is that ellipsoidal intersection can be computed with the aid of the Bar-Shalom/Campo fusion formulae. This is achieved by assuming a specific correlation structure between the estimates to be fused.

    @inproceedings{MFI16_Noack,
      title = {{Algebraic Analysis of Data Fusion with Ellipsoidal Intersection}},
      author = {Noack, Benjamin and Sijs, Joris and Hanebeck, Uwe D.},
      booktitle = {Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016)},
      year = {2016},
      address = {Baden-Baden, Germany},
      month = {sep},
    }
    
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