Noack
Prof. Dr.-Ing. Benjamin Noack
Institute for Intelligent Cooperating Systems
Current projects
Intelligent Mobility Space in the District
Duration: 01.01.2024 bis 31.12.2027
Summary
“IMIQ – Intelligent Mobility Space in the District” is a project of the IMR – Intelligent Mobility Space Saxony Anhalt (https://niimo.ovgu.de/en/Intelligent+Mobility+Space.html), which will be based in the Science Harbor in Magdeburg. Over a period of 3 1/2 years (01/2024 - 12/2027, actual operational start 8/2024), the science harbor will become a future district in which new solutions will be developed in a needs-oriented manner, tested technically and informationally and implemented socio-economically . Key innovations include a digital work-life twin (DMLZ) and a real-world laboratory for intelligent mobility (RIM).
Ambitions
The aim is to develop and test innovative mobility and communication approaches. A digital work-life twin (DWLZ) enables a holistic and innovative mobility and communication experience that offers efficient and personalized solutions through sensors, 5G and digital services and at the same time promotes social interaction and exchange on site. In the Intelligent Mobility Real Laboratory (RIM), the researchers' developments on intelligent mobility become physically visible and tangible/experienceable; they are tested and evaluated. Technologies for communication and V2X, localization and tracking are controlled in an operation control center, integrated with infrastructure (including mobility stations) and with implemented autonomous vehicles.
Further Information
You can find a detailed description, news and staff positions here: https://niimo.ovgu.de/IMIQ.html. Under this link, or under the names linked above, you will also find information about the IMIQ work areas of the project partners.
This project will build up cutting-edge research in the interdisciplinary research field of mobility at the OVGU and enable the transfer of new mobility solutions in Saxony-Anhalt and beyond. The visibility or experience is aimed at all stakeholders.
Modular peristaltic surface conveyor with AI based digital twin for polybags
Duration: 01.04.2024 bis 31.12.2027
The Modular Peristaltic Surface Conveyor (MPSC) is an entirely new device that conceptually enables the separation and sorting of flexible small packages (polybags) for the first time, providing an alternative to costly manual processing. For the first time, alongside the development of the actual MPSC, an AI-based Digital Twin (DT) is to be developed, which, based on AI-optimized simulation models, will allow predictions of system behavior and automated parameterization of the actuators and sensor data processing.
SeaSentry - Development of a Real-Time Capable Land-Based Ship Tracking System to Increase Maritime Safety
Duration: 01.09.2024 bis 31.08.2027
SeaSentry aims to develop a land-based sensor network for passive detection and real-time localization of ship movements, which functions without requiring additional installations onboard. This technology enhances and improves existing monitoring systems, providing an innovative solution for maritime traffic control.
A key aspect of the project is the testing of the sensor technology in the eMIR testbed in the German Bight, which extends from the Elbe estuary to the port of Emden. This test area offers a variety of maritime scenarios to evaluate the technology under real conditions. To test applications with greater range, the eMIR testbed will be extended to the Heligoland site.
The developed technology will be integrated into VTS systems to improve efficiency and safety in maritime environments. The passively operating sensor network enables reliable detection of ships without the need for additional devices onboard. SeaSentry thus contributes to maritime safety and has the potential to revolutionize the monitoring of ship movements in complex environments.
At Otto von Guericke University Magdeburg, researchers are developing algorithms to localize and track ships using the SeaSentry sensor network. Initially, their focus is on enhancing signal processing techniques, particularly peak detection. The peak times identified at each sensor node will be converted into position estimates for individual ships. By incorporating motion models and dynamic estimation methods, a comprehensive tracking system will be created. This system will include track management, measurement assignments, and uncertainty assessments for each ship to ensure the required accuracy for the overall project. In the second phase, methods for optimizing sensor placement will be developed.
TACTIC
Duration: 01.01.2024 bis 01.02.2027
Scientific goals
The idea of co-evolution at the human-technology interface is based on the fact that both the biological side and the technical side of an interface are not only dynamic and adaptive, but also take account of the other side in their adaptivity. Investigating this mutual influence leads to a deeper understanding of the causes of undesired processes, such as the maladaptation of inflammatory responses to unwanted changes in implant surfaces. This understanding then opens up new strategies to support desired processes in the sense of co-evolution. These include the possibilities of adaptive technologies and sensor approaches that can adjust to individual dynamics in the biological system, or the development of process-aware technologies that can bring about desired dynamics in the biological system.
Intended strategic goals
The modules of the TACTIC graduate school are designed to enhance translational expertise in the fields of medical technology, sensor technology, and artificial intelligence (AI). The goal is to strengthen research, development, and innovation activities on site. The aim is to closely interlink life sciences and engineering across all modules in order to facilitate future collaborative projects in this area. In addition, the integration of AI is intended to strengthen the profile area of medical technology. By internationalizing the research focus areas, TACTIC enables networking with EU partners, which is an important prerequisite for the alignment of consortia in order to strengthen science in Saxony-Anhalt.
Work program
The graduate school comprises three modules with a total of 9 doctoral students. A thematic network is established through doctoral topics, where at least two thematic modules are assigned concurrently. Each of the three thematic modules - Interaction, AI and Interface - is endowed with three doctoral positions (100%). The aim is to qualify our doctoral students for both the academic and private sector job markets. Interdisciplinary skills are to be imparted through doctoral seminars. Annual thesis committee meetings and TACTIC symposia support the development of doctoral students. An international network is to be established through presentations at international conferences and self-organized symposia.
Lazy Estimation in Networked Systems
Duration: 17.04.2023 bis 16.04.2026
The amount of sensor data provided by battery-driven, widely distributed devices is steadily increasing. Since sensor data are typically fed into information processing units, it is worth considering how information processing itself can be exploited to reduce communication and energy demands. For this purpose, this project focuses on information-processing techniques that can incorporate implicit information conveyed by the transmission mechanism. Although a sensor node decides not to send its data, the receiver can still leverage the absence of data to update its state estimates. For instance, sensor readings can be compared against a threshold to decide for a transmission. The receiver can translate this decision rule into information about the data although no transmission took place. Sender and receiver can negotiate such decision rules in order to minimize communication costs, on the transmitting end, and to maximize the retrievable information, on the receiving end. Since threshold-based strategies are far too restrictive for time-varying systems being observed, model-based and data-driven policies will be investigated.
This project primarily investigates stochastic decision rules to trigger transmissions. In contrast to deterministic triggers, stochastic mechanisms can preserve the Gaussianity of the implicit information simplifying the estimator design at the receiver. For instance, a Kalman filter only requires minor adaptions to incorporate implicit information when no transmission event is triggered. The goal of this project is to push the principles of stochastic triggering forward to establish a comprehensive framework of lazy estimation. First, the investigations are concerned with general properties and the design of intelligent trigger decisions to improve the effectiveness and robustness of lazy state estimation. These include model-based and data-driven trigger mechanisms, aperiodic and asynchronous transmission and processing times, as well as the study of unreliable communication links. The results provide the foundations for large-scale lazy estimation with respect to both multisensor systems and high-dimensional state representations. For instance, multiple systems collaboratively monitor a dynamic system and fuse exchanged sensor data and estimates. Such distributed data fusion problems lead to dependent trigger decisions that require self-adapting trigger mechanisms. In particular, the project considers applications in object tracking to evaluate the derived concepts. Lazy estimation shows great potential in the processing of neuromorphic sensor data.
DatAmount - Modelling the Energy and Resource Consumption of Machine Tools Using Intelligent and Data-Efficient Methods
Duration: 01.03.2023 bis 31.08.2025
As part of the DatAmount research project, methods are being developed that make it possible to create energy models of machine tools. These models are suitable for predicting the energetic behavior of machines for new products on the basis of small amounts of data. Since small series are often produced, especially in the SME context, in many cases, there is not enough data to train AI models. Physical modeling, on the other hand, is often very costly. Due to the required CO2 proofs and the climate targets set, companies thus find themselves in a field of tension. On the one hand, accurate models to predict the energy consumption of machines are necessary to remain competitive. On the other hand, the creation of such models is currently either very expensive or not possible. The current mostly manual prediction of energy consumption is also time-consuming and also person-bound. The approach presented here combines physical models of the energy behavior of machines with data-based machine learning models, with particularly data-efficient machine learning models being investigated. This enables an automated, accurate prediction of the energy consumption of machine tools. The benefit for SMEs is the efficient creation of models that can predict the energy consumption and CO2 emissions of new products. These predictions are often necessary to be considered in a tender as proof of energy and resource efficiency is often mandatory in bids from larger companies with CO2 reduction targets.
Ready for Smart City Robots? Multimodal Maps for Autonomous Micromobiles - R4R
Duration: 01.06.2022 bis 31.05.2025
Problem Definition
Autonomously operating mobility systems or delivery services open up considerable development potential with regard to quality of life and services of general interest in non-urban areas such as in the former brown coal regions of Germany. However, assessing the potential success of micromobiles operating autonomously on footpaths and cycle paths requires comprehensive environmental information from the areas of operation, such as minimum path widths, the volume of foot traffic or sight lines. In particular, outside of large cities, this information is incomplete and heterogeneously structured.
Project Objectives
The aim of the project is to design strategies for the bicycle-based collection of environmental data that are relevant for the successful operation of autonomous micromobiles on footpaths (visibility of certain areas, infrastructure parameters, passenger volume, network coverage, environmental data). For this purpose, the project evaluates different collection methods with regard to the efficiency and quality of the aggregated information. The usability of the data will be examined in two concrete smart city/town application scenarios (rental bicycles with autonomous delivery mode and delivery robots) with corresponding studies. In this way, the project contributes to the data-driven development of smart mobility and logistics concepts that cover the specific features of different settlement areas.
2025
Book chapter
Finding predictive features for energy consumption of CNC machines
Kader, Hafez; Ströbel, Robin; Puchta, Alexander; Fleischer, Jürgen; Noack, Benjamin; Spiliopoulou, Myra
In: GFaI Tagungsband 2024 - Berlin : Gesellschaft zur Förderung angewandter Informatik e.V., S. 89-95 [Workshop: 26. Anwenderbezogener Workshop zur Erfassung, Modelierung, Verarbeitung und Auswertung von 3D-Daten, Berlin, 26. -27. November 2024]
Dissertation
Der Entwicklungsprozess automatisierter Mikromobile - ein mechatronischer Ansatz
Junge, Lars; Noack, Benjamin
In: Magdeburg: Universitätsbibliothek, Dissertation Otto-von-Guericke-Universität Magdeburg, Fakultät für Maschinenbau 2025, 1 Online-Ressource (II, 183 Seiten, 16,7 MB) [Literaturverzeichnis: Seite 162-167][Literaturverzeichnis: Seite 162-167]
2024
Book chapter
Simultaneous gas exploration and network localization with robotic swarms
Broghammer, Fabio; Wiedemann, Thomas; Zhang, Siwei; Noack, Benjamin
In: 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) - Piscataway, NJ : IEEE, insges. 5 S. [Konferenz: IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW, Seoul, Korea, 14-19 April 2024]
Event-based multisensor fusion with correlated estimates
Schmitt, Eva Julia; Noack, Benjamin
In: 2024 27th International Conference on Information Fusion (FUSION) - [Piscataway, NJ] : IEEE, insges. 8 S. [Konferenz: 27th International Conference on Information Fusion (FUSION), Venice, Italy, 08-11 July 2024]
Consistent stochastic event-based estimation under packet losses using low-cost sensors
Schmitt, Eva Julia; Noack, Benjamin
In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) - [Piscataway, NJ] : IEEE . - 2024, insges. 7 S. [Konferenz: 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, Pilsen, Czech Republic, 04-06 September 2024]
Feature ranking for the prediction of energy consumption on CNC machining processes
Kader, Hafez; Ströbel, Robin; Puchta, Alexander; Fleischer, Jürgen; Noack, Benjamin; Spiliopoulou, Myra
In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) - [Piscataway, NJ] : IEEE . - 2024, insges. 7 S. [Konferenz: 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, Pilsen, Czech Republic, 04-06 September 2024]
Conservative compression of information matrices using event-triggering and robust optimization
Funk, Christopher; Noack, Benjamin
In: 2024 27th International Conference on Information Fusion (FUSION) - [Piscataway, NJ] : IEEE, insges. 8 S. [Konferenz: 27th International Conference on Information Fusion (FUSION), Venice, Italy, 08-11 July 2024]
Peer-reviewed journal article
An event-based approach for the conservative compression of covariance matrices
Funk, Christopher; Noack, Benjamin
In: IEEE transactions on automatic control / Institute of Electrical and Electronics Engineers - New York, NY : Institute of Electrical and Electronics Engineers . - 2024, insges. 13 S. [Online first]
A quarter century of covariance intersection - correlations still unknown?
Forsling, Robin; Noack, Benjamin; Hendeby, Gustaf
In: IEEE control systems magazine / Institute of Electrical and Electronics Engineers - New York, NY : IEEE, Bd. 44 (2024), Heft 2, S. 81-105
Dissertation
Cost-aware adaptive sampling for environmental sensing
Westermann, Johannes; Noack, Benjamin
In: Magdeburg: Universitätsbibliothek, Dissertation Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik 2024, 1 Online-Ressource (xvii, 118 Seiten, 2.64 MB) [Literaturverzeichnis: Seite 105-118][Literaturverzeichnis: Seite 105-118]
2023
Book chapter
Classification of uncertainty sources for reliable Bayesian estimation
Duník, Jinřich; Straka, Ondřej; Noack, Benjamin
In: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) , 2023 - [Piscataway, NJ] : IEEE, insges. 8 S. [Symposium: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI), Bonn, Germany, 27-29 November 2023]
Event-based colored-noise Kalman filtering for improved resource effiency
Schmitt, Eva; Noack, Benjamin
In: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) , 2023 - [Piscataway, NJ] : IEEE, insges. 7 S. [Symposium: 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI), Bonn, Germany, 27-29 November 2023]
Statistical approach for preload monitoring of ball screw drives
Mayer, Jana; Klumpp, Vesa; Hillenbrand, Jonas; Noack, Benjamin
In: IEEE SENSORS 2023 , 2023 - Piscataway, NJ, USA : IEEE, insges. 4 S. [Konferenz: 2023 IEEE SENSORS, Vienna, Austria, 29 October 2023 - 01 November 2023]
Graduated moving Window optimization as a flexible framework for multi-object tracking
Funk, Christopher; Noack, Benjamin
In: 2023 American Control Conference (ACC) , 2023 - [Piscataway, NJ] : IEEE ; Tan, Xiaobo, S. 4864-4870 [Konferenz: 2023 American Control Conference (ACC), San Diego, CA, USA, 31 May 2023 - 02 June 2023]
Conservative data reduction for covariance matrices using elementwise event triggers
Funk, Christopher; Noack, Benjamin
In: FUSION 2023 / International Conference on Information Fusion , 2023 - [Piscataway, NJ] : IEEE, insges. 6 S.
Productive teaming under uncertainty: when a human and a machine classify objects together
Rother, Anne; Notni, Gunther; Hasse, Alexander; Noack, Benjamin; Beyer, Christian; Reißmann, Jan; Zhang, Chen; Ragni, Marco; Arlinghaus, Julia C.; Spiliopoulou, Myra
In: 2023 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO) , 2023 - [Piscataway, NJ] : IEEE, S. 9-14
Peer-reviewed journal article
Discriminative feature learning through feature distance loss
Schlagenhauf, Tobias; Lin, Yiwen; Noack, Benjamin
In: Machine vision and applications - Berlin : Springer, Bd. 34 (2023), Heft 2, Artikel 25, insges. 13 S.
Distributed range-only localization that preserves sensor and navigator privacies
Ristic, Marko; Noack, Benjamin; Hanebeck, Uwe D.
In: IEEE transactions on automatic control / Institute of Electrical and Electronics Engineers - New York, NY : Institute of Electrical and Electronics Engineers, Bd. 68 (2023), Heft 12, S. 7151-7163
Receding horizon cost-aware adaptive sampling for environmental monitoring
Westermann, Johannes; Mayer, Jana; Petereit, Janko; Noack, Benjamin
In: IEEE control systems letters - New York, NY : IEEE, Bd. 7 (2023), S. 1069-1074
2022
Book chapter
Privileged estimate fusion with correlated Gaussian keystreams
Ristic, Marko; Noack, Benjamin
In: CDC 22 / IEEE Conference on Decision and Control , 2022 - [Piscataway, NJ] : IEEE, S. 7732-7739 [Konferenz: IEEE 61st Conference on Decision and Control, CDC, Cancun, Mexico, 06-09 December 2022]
Event-based Kalman filtering exploiting correlated trigger information
Noack, Benjamin; Öhl, Clemens; Hanebeck, Uwe D.
In: 2022 25th International Conference on Information Fusion (FUSION) , 2022 - Piscataway, NJ : IEEE, insges. 8 S. [Konferenz: 25th International Conference on Information Fusion, FUSION, Linköping, Sweden, 04-07 July 2022]
Encrypted fast covariance intersection without leaking fusion weights
Ristic, Marko; Noack, Benjamin
In: International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) / IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems , 2022 - [Piscataway, NJ] : IEEE, insges. 6 S. [Konferenz: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI, Bedford, United Kingdom, 20-22 September 2022]
2021
Book chapter
Kalman filtered compressive sensing using pseudo-measurements
Zhao, Haibin; Funk, Christopher; Noack, Benjamin; Hanebeck, Uwe; Beigl, Michael
In: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2021) - Karlsruhe - 2021, paper 66 [Konferenz: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2021, Karlsruhe, Germany, 23-25 September 2021]
Peer-reviewed journal article
Fully Decentralized Estimation Using Square-Root Decompositions
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Journal of Advances in Information Fusion, Bd. 16, 1, S. 3-16, 2021
Secure Fast Covariance Intersection Using Partially Homomorphic and Order Revealing Encryption Schemes
Ristic, Marko; Noack, Benjamin; Hanebeck, Uwe D.
In: IEEE Control Systems Letters, Institute of Electrical and Electronics Engineers (IEEE), 2021, Bd. 5, Heft 1, S. 217-222
Cryptographically privileged state estimation with Gaussian keystreams
Ristic, Marko; Noack, Benjamin; Hanebeck, Uwe D.
In: IEEE control systems letters - New York, NY : IEEE, Bd. 6 (2021), S. 602-607
Conservative quantization of covariance matrices with applications to decentralized information fusion
Funk, Christopher; Noack, Benjamin; Hanebeck, Uwe D.
In: Sensors - Basel : MDPI, Bd. 21 (2021), Heft 9, Artikel 3059, insges. 21 S.
2020
Peer-reviewed journal article
Predictive Tracking with Improved Motion Models for Optical Belt Sorting
Pfaff, Florian; Pieper, Christoph; Maier, Georg; Noack, Benjamin; Gruna, Robin; Kruggel-Emden, Harald; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen
In: In: at -- Automatisierungstechnik, Bd. 4, 2020
Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking
Maier, Georg; Pfaff, Florian; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Beyerer, Jürgen
In: In: Transactions on Industrial Electronics, 2020
Characterizing Material Flow in Sensor-Based Sorting Systems Using an Instrumented Particle
Maier, Georg; Pfaff, Florian; Bittner, Andrea; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Hanebeck, Uwe D.; Längle, Thomas; Beyerer, Jürgen
In: In: at -- Automatisierungstechnik, Bd. 4, 2020
Article in conference proceedings
Fully Decentralized Estimation Using Square-Root Decompositions
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 23rd International Conference on Information Fusion (Fusion 2020), 2020
Conservative Quantization of Fast Covariance Intersection
Funk, Christopher; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), 2020
Improved Pose Graph Optimization for Planar Motions Using Riemannian Geometry on the Manifold of Dual Quaternions
Li, Kailai; Cox, Johannes; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 21st IFAC World Congress (IFAC 2020), 2020
Reconstruction of Cross-Correlations between Heterogeneous Trackers Using Deterministic Samples
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 21st IFAC World Congress (IFAC 2020), 2020
State Estimation with Event-Based Inputs Using Stochastic Triggers
Noack, Benjamin; Funk, Christopher; Radtke, Susanne; Hanebeck, Uwe D.
In: In: Proceedings of the 21st IFAC World Congress (IFAC 2020), 2020
2019
Peer-reviewed journal article
Gaussianity-Preserving Event-Based State Estimation with an FIR-Based Stochastic Trigger
Schmitt, Eva Julia; Noack, Benjamin; Krippner, Wolfgang; Hanebeck, Uwe D.
In: In: IEEE Control Systems Letters, Bd. 3, 3, S. 769-774, 2019, 2475-1456
Article in conference proceedings
Feature-Aided Multitarget Tracking for Optical Belt Sorters
Kronauer, Tobias; Pfaff, Florian; Noack, Benjamin; Tian, Wei; Maier, Georg; Hanebeck, Uwe D.
In: In: Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019), 2019
State Estimation with Model-Mismatch-Based Secrecy against Eavesdroppers
Özgen, Selim; Kohn, Saskia; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019), 2019
Distributed Estimation with Partially Overlapping States based on Deterministic Sample-based Fusion
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2019 European Control Conference (ECC 2019), 2019
Consistent Fusion in Networks Using Square-root Decompositions of Correlations
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019), 2019
Geometry-Driven Deterministic Sampling for Nonlinear Bingham Filtering
Li, Kailai; Frisch, Daniel; Noack, Benjamin; Hanebeck, Uwe
In: In: Proceedings of the 2019 European Control Conference (ECC 2019), 2019
Nonlinear Decentralized Data Fusion with Generalized Inverse Covariance Intersection
Noack, Benjamin; Orguner, Umut; Hanebeck, Uwe D.
In: In: Proceedings of the 22nd International Conference on Information Fusion (Fusion 2019), 2019
Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements
Radtke, Susanne; Li, Kailai; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2019 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2019), 2019
2018
Book chapter
State Estimation in Networked Control Systems with Delayed and Lossy Acknowledgments
Rosenthal, Florian; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System, Springer International Publishing, S. 22-38, 2018, 978-3-319-90509-9
Peer-reviewed journal article
Optimally Distributed Kalman Filtering with Data-Driven Communication
Dormann, Katharina; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Sensors, Bd. 18, 4, 2018, 1424-8220
Numerical Modelling of an Optical Belt Sorter Using a DEMCFD Approach Coupled with Particle Tracking and Comparison with Experiments
Pieper, Christoph; Pfaff, Florian; Maier, Georg; Kruggel-Emden, Harald; Wirtz, Siegmar; Noack, Benjamin; Gruna, Robin; Scherer, Viktor; Hanebeck, Uwe D.; Längle, Thomas; Beyerer, Jürgen
In: In: Powder Technology, Bd. 370, S. 181-193, 2018
On Directional Splitting of Gaussian Density in Nonlinear Random Variable Transformation
Duník, Jindich; Straka, Ondej; Noack, Benjamin; Steinbring, Jannik; Hanebeck, Uwe D.
In: In: IET Signal Processing, 2018, 1751-9683
Article in conference proceedings
Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples
Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.; Straka, Ondej
In: In: Proceedings of the 21st International Conference on Information Fusion (Fusion 2018), 2018
Encrypted Multisensor Information Filtering
Aristov, Mikhail; Noack, Benjamin; Hanebeck, Uwe D.; Müller-Quade, Jörn
In: In: Proceedings of the 21st International Conference on Information Fusion (Fusion 2018), 2018
Wavefront Orientation Estimation Based on Progressive Bingham Filtering
Li, Kailai; Frisch, Daniel; Radtke, Susanne; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2018), 2018
Retrodiction of Data Association Probabilities via Convex Optimization
Özgen, Selim; Hanebeck, Uwe D.; Noack, Benjamin; Huber, Marco; Rosenthal, Florian; Mayer, Jana
In: In: Proceedings of the 21st International Conference on Information Fusion (Fusion 2018), 2018
Scheduling of Measurement Transmission in Networked Control Systems Subject to Communication Constraints
Rosenthal, Florian; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2018 American Control Conference (ACC 2018), 2018
2017
Peer-reviewed journal article
Real-Time Multitarget Tracking for Sensor-Based Sorting
Maier, Georg; Pfaff, Florian; Wagner, Matthias; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Beyerer, Jürgen
In: In: Journal of Real-Time Image Processing, 2017
Decentralized Data Fusion with Inverse Covariance Intersection
Noack, Benjamin; Sijs, Joris; Reinhardt, Marc; Hanebeck, Uwe D.
In: In: Automatica, Bd. 79, S. 35-41, 2017
Real-Time Motion Prediction Using the Chromatic Offset of Line Scan Cameras
Pfaff, Florian; Maier, Georg; Aristov, Mikhail; Noack, Benjamin; Gruna, Robin; Hanebeck, Uwe D.; Längle, Thomas; Beyerer, Jürgen; Pieper, Christoph; Kruggel-Emden, Harald; Wirtz, Siegmar; Scherer, Viktor
In: In: at - Automatisierungstechnik, De Gruyter, 2017
Motion-Based Material Characterization in Sensor-Based Sorting
Maier, Georg; Pfaff, Florian; Becker, Florian; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Beyerer, Jürgen
In: In: tm - Technisches Messen, De Gruyter, 2017
Article in conference proceedings
Event-Based Estimation in a Feedback Loop Anticipating on Imperfect Communication
Sijs, Joris; Noack, Benjamin
In: In: Proceedings of the 20th IFAC World Congress (IFAC 2017), 2017
Improving Multitarget Tracking Using Orientation Estimates for Sorting Bulk Materials
Pfaff, Florian; Kurz, Gerhard; Pieper, Christoph; Maier, Georg; Noack, Benjamin; Kruggel-Emden, Harald; Gruna, Robin; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen
In: In: Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), 2017
State Estimation in Networked Control Systems With Delayed And Lossy Acknowledgments
Rosenthal, Florian; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), 2017
Optimal Distributed Combined Stochastic and Set-Membership State Estimation
Pfaff, Florian; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 20th International Conference on Information Fusion (Fusion 2017), 2017
Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information
Maier, Georg; Pfaff, Florian; Becker, Florian; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Beyerer, Jürgen
In: In: Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), 2017
Distributed Kalman Filtering With Reduced Transmission Rate
Dormann, Katharina; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017), 2017
Inverse Covariance Intersection: New Insights and Properties
Noack, Benjamin; Sijs, Joris; Hanebeck, Uwe D.
In: In: Proceedings of the 20th International Conference on Information Fusion (Fusion 2017), 2017
Information Form Distributed Kalman Filtering (IDKF) with Explicit Inputs
Pfaff, Florian; Noack, Benjamin; Hanebeck, Uwe D.; Govaers, Felix; Koch, Wolfgang
In: In: Proceedings of the 20th International Conference on Information Fusion (Fusion 2017), 2017
Numerical Modelling of the Separation of Complex Shaped Particles in an Optical Belt Sorter Using a DEM--CFD Approach and Comparison with Experiments
Pieper, Christoph; Maier, Georg; Pfaff, Florian; Kruggel-Emden, Harald; Gruna, Robin; Noack, Benjamin; Wirtz, Siegmar; Scherer, Viktor; Längle, Thomas; Hanebeck, Uwe D.; Beyerer, Jürgen
In: In: V International Conference on Particle-based Methods. Fundamentals and Applications (PARTICLES 2017), 2017
2016
Peer-reviewed journal article
Improving Optical Sorting of Bulk Materials Using Sophisticated Motion Models
Pfaff, Florian; Pieper, Christoph; Maier, Georg; Noack, Benjamin; Kruggel-Emden, Harald; Gruna, Robin; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen
In: In: tm - Technisches Messen, De Gruyter, Bd. 83, 2, S. 77-84, 2016
Numerical Modeling of an Automated Optical Belt Sorter using the Discrete Element Method
Pieper, Christoph; Maier, Georg; Pfaff, Florian; Kruggel-Emden, Harald; Wirtz, Siegmar; Gruna, Robin; Noack, Benjamin; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen; Hanebeck, Uwe D.
In: In: Powder Technology, 2016
Article in conference proceedings
State Estimation Considering Negative Information with Switching Kalman and Ellipsoidal Filtering
Noack, Benjamin; Pfaff, Florian; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 19th International Conference on Information Fusion (Fusion 2016), 2016
Simulation-based Evaluation of Predictive Tracking for Sorting Bulk Materials
Pfaff, Florian; Pieper, Christoph; Maier, Georg; Noack, Benjamin; Kruggel-Emden, Harald; Gruna, Robin; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Längle, Thomas; Beyerer, Jürgen
In: In: Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), 2016
Algebraic Analysis of Data Fusion with Ellipsoidal Intersection
Noack, Benjamin; Sijs, Joris; Hanebeck, Uwe D.
In: In: Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), 2016
Camera- and IMU-based Pose Tracking for Augmented Reality
Faion, Florian; Zea, Antonio; Noack, Benjamin; Steinbring, Jannik; Hanebeck, Uwe D.
In: In: Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), 2016
Fast Multitarget Tracking via Strategy Switching for Sensor-Based Sorting
Maier, Georg; Pfaff, Florian; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel-Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Wirtz, Siegmar; Scherer, Viktor; Beyerer, Jürgen
In: In: Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), 2016
Numerical Investigation of Optical Sorting using the Discrete Element Method
Pieper, Christoph; Kruggel-Emden, Harald; Wirtz, Siegmar; Scherer, Viktor; Pfaff, Florian; Noack, Benjamin; Hanebeck, Uwe D.; Maier, Georg; Gruna, Robin; Längle, Thomas; Beyerer, Jürgen
In: In: Proceedings of the 7th International Conference on Discrete Element Methods (DEM7), 2016
Optimal Sample-Based Fusion for Distributed State Estimation
Steinbring, Jannik; Noack, Benjamin; Reinhardt, Marc; Hanebeck, Uwe D.
In: In: Proceedings of the 19th International Conference on Information Fusion (Fusion 2016), 2016
2015
Book chapter
Time-Periodic State Estimation with Event-Based Measurement Updates
Sijs, Joris; Noack, Benjamin; Lazar, Mircea; Hanebeck, Uwe D.
In: In: Event-Based Control and Signal Processing, CRC Press, S. 261-279, 2015
Treatment of Dependent Information in Multisensor Kalman Filtering and Data Fusion
Noack, Benjamin; Sijs, Joris; Reinhardt, Marc; Hanebeck, Uwe D.
In: In: Multisensor Data Fusion: From Algorithms and Architectural Design to Applications, CRC Press, S. 169-192, 2015
Peer-reviewed journal article
Minimum Covariance Bounds for the Fusion under Unknown Correlations
Reinhardt, Marc; Noack, Benjamin; Arambel, Pablo O.; Hanebeck, Uwe D.
In: In: IEEE Signal Processing Letters, Bd. 22, 9, S. 1210 - 1214, 2015
Article in conference proceedings
Kalman Filter-based SLAM with Unknown Data Association using Symmetric Measurement Equations
Baum, Marcus; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), 2015
TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials
Pfaff, Florian; Baum, Marcus; Noack, Benjamin; Hanebeck, Uwe D.; Gruna, Robin; Längle, Thomas; Beyerer, Jürgen
In: In: Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Information Integration (MFI 2015), 2015
Treatment of Biased and Dependent Sensor Data in Graph-based SLAM
Noack, Benjamin; Julier, Simon J.; Hanebeck, Uwe D.
In: In: Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), 2015
State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-membership Pseudo Measurements
Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), 2015
2014
Article in conference proceedings
On Nonlinear Track-to-track Fusion with Gaussian Mixtures
Noack, Benjamin; Reinhardt, Marc; Hanebeck, Uwe D.
In: In: Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), 2014
Covariance Intersection in State Estimation of Dynamical Systems
Ajgl, Jií; imandl, Miroslav; Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), 2014
Reconstruction of Joint Covariance Matrices in Networked Linear Systems
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014), 2014
Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering
Noack, Benjamin; Sijs, Joris; Hanebeck, Uwe D.
In: In: Proceedings of the 19th IFAC World Congress (IFAC 2014), 2014
Distributed Kalman Filtering in the Presence of Packet Delays and Losses
Reinhardt, Marc; Noack, Benjamin; Kulkarni, Sanjeev; Hanebeck, Uwe D.
In: In: Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), 2014
A Study on Event Triggering Criteria for Estimation
Sijs, Joris; Kester, Leon; Noack, Benjamin
In: In: Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), 2014
2013
Dissertation
State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties
Noack, Benjamin
In: 2013
Article in conference proceedings
Data Validation in the Presence of Stochastic and Set-membership Uncertainties
Pfaff, Florian; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), 2013
Nonlinear Federated Filtering
Noack, Benjamin; Julier, Simon J.; Reinhardt, Marc; Hanebeck, Uwe D.
In: In: Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), 2013
An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation
Sijs, Joris; Hanebeck, Uwe D.; Noack, Benjamin
In: In: Proceedings of the 2013 European Control Conference (ECC 2013), 2013
Advances in Hypothesizing Distributed Kalman Filtering
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), 2013
Event-based State Estimation with Negative Information
Sijs, Joris; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), 2013
2012
Article in conference proceedings
Closed-form Optimization of Covariance Intersection for Low-dimensional Matrices
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), 2012
The Hypothesizing Distributed Kalman Filter
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012), 2012
Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation
Noack, Benjamin; Pfaff, Florian; Hanebeck, Uwe D.
In: In: Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 2012
Combined Stochastic and Set-membership Information Filtering in Multisensor Systems
Noack, Benjamin; Pfaff, Florian; Hanebeck, Uwe D.
In: In: Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), 2012
On Optimal Distributed Kalman Filtering in Non-ideal Situations
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), 2012
Decentralized Control Based on Globally Optimal Estimation
Reinhardt, Marc; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), 2012
Pushing Kalman's Idea to the Extremes
Benavoli, Alessio; Noack, Benjamin
In: In: Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), 2012
2011
Article in conference proceedings
Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities
Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), 2011
Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations
Reinhardt, Marc; Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), 2011
Random Hypersurface Mixture Models for Tracking Multiple Extended Objects
Baum, Marcus; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), 2011
Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
Baum, Marcus; Noack, Benjamin; Beutler, Frederik; Itte, Dominik; Hanebeck, Uwe D.
In: In: Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), 2011
Nonlinear Information Filtering for Distributed Multisensor Data Fusion
Noack, Benjamin; Lyons, Daniel; Nagel, Matthias; Hanebeck, Uwe D.
In: In: Proceedings of the 2011 American Control Conference (ACC 2011), 2011
An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks
Schmid, Johannes; Beutler, Frederik; Noack, Benjamin; Hanebeck, Uwe D.; Müller-Glaser, Klaus D.
In: In: Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011), Springer, Bd. 6567, S. 147-162, 2011
Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 18th IFAC World Congress (IFAC 2011), 2011
2010
Book chapter
Systematische Beschreibung von Unsicherheiten in der Informationsfusion mit Mengen von Wahrscheinlichkeitsdichten
Noack, Benjamin; Klumpp, Vesa; Lyons, Daniel; Hanebeck, Uwe D.
In: In: Verteilte Messsysteme, KIT Scientific Publishing, S. 167-178, 2010
Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung
Lyons, Daniel; Hekler, Achim; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Verteilte Messsysteme, KIT Scientific Publishing, S. 121-132, 2010
Peer-reviewed journal article
Modellierung von Unsicherheiten und Zustandsschätzung mit Mengen von Wahrscheinlichkeitsdichten
Noack, Benjamin; Klumpp, Vesa; Lyons, Daniel; Hanebeck, Uwe D.
In: In: tm - Technisches Messen, Oldenbourg Verlag, Bd. 77, 10, S. 544-550, 2010
Article in conference proceedings
Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities
Hekler, Achim; Lyons, Daniel; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), 2010
A Log-Ratio Information Measure for Stochastic Sensor Management
Lyons, Daniel; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010), 2010
Extended Object and Group Tracking with Elliptic Random Hypersurface Models
Baum, Marcus; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), 2010
Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter
Klumpp, Vesa; Noack, Benjamin; Baum, Marcus; Hanebeck, Uwe D.
In: In: Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), 2010
Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering
Noack, Benjamin; Klumpp, Vesa; Petkov, Nikolay; Hanebeck, Uwe D.
In: In: Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), 2010
Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties
Bogatyrenko, Evgeniya; Noack, Benjamin; Hanebeck, Uwe D.
In: In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), 2010
2009
Article in conference proceedings
State Estimation with Sets of Densities considering Stochastic and Systematic Errors
Noack, Benjamin; Klumpp, Vesa; Hanebeck, Uwe D.
In: In: Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), 2009
2008
Article in conference proceedings
Nonlinear Bayesian Estimation with Convex Sets of Probability Densities
Noack, Benjamin; Klumpp, Vesa; Brunn, Dietrich; Hanebeck, Uwe D.
In: In: Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), S. 1-8, 2008
Multisensor data fusion is the process of combining data streams from multiple – possibly heterogeneous and complementary – sensors. As a result, systems like autonomous mobile robots benefit from increased data quality and reliability, can estimate unmeasured states, and can cover larger areas with their sensors. Our research includes
- Kalman filtering and nonlinear estimation
- Modeling and uncertainty quantification
- Algorithms for localization, tracking, and situational awareness
- Decentralized data fusion in mobile systems and ad-hoc sensor networks
Resource-Efficient Sensor Data Processing
Mobile sensor systems typically have resource constraints in processing capabilities, battery power, and storage space. Also, communication bandwidth is a limiting factor when multiple systems need to exchange sensor data. Multisensor data fusion can address these limitations by different measures. Our research includes
- Reduced-complexity fusion algorithms
- Adaptive sampling rates and asynchronous processing
- Event-based sensor fusion and estimation
- Quantization of sensor data and estimates
Localization with Mutual Dependencies
Precise localization and tracking of vehicles such as drones or cars is essential for their safe autonomous operation, particularly when they operate in constrained shared spaces and must therefore interact with one another. Decentralized methods for combining information distributed across several vehicles and exploiting mutual dependencies are beneficial in such scenarios. Our research includes
- Optimization-based state estimation
- Utilization of mutual dependencies
- Decentralized optimization algorithms
- Spatially distributed measurements
Privacy-Preserving Data Fusion
With the advancements in distributed algorithms and cloud computing, the reliance on public communication channels and untrusted participants has stressed the requirement for data privacy. Privacy-preserving data fusion presents methods for increasing participants' data quality in distributed environments while keeping their sensitive data private. This includes private state estimates, exact measurements and fixed locations. Our research includes
- Privacy-preserving distributed localization
- Homomorphic and functional encrypted cloud data fusion
- Cryptographic privileges in estimation
- Quantifiable data fusion leakage
- Covadonga GmbH
- CyFace GmbH, Dresden
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
- DigiPL GmbH, Halle (Saale)
- Endiio Engineering GmbH, Freiburg
- Gesellschaft zur Förderung angewandter Informatik e.V. (GFaI)
- Hochschule Anhalt, Köthen
- Hochschule Merseburg
- in-innovative navigation GmbH
- Knowtion GmbH
- Landkreis Nordsachsen
- PTV AG, Karlsruhe
- Technische Universität Bergakademie, Freiberg
- TINK GmbH, Konstanz
- wbk Institut für Produktionstechnik, Karlsruher Institut für Technology (KIT)
Teaching
- Advanced Estimation Methods for Autonomous Robotic Systems ( Link zur LV im LSF )
- AMS Lab Projects ( Link zur LV im LSF )
- Introduction to Distributed Sensor Data Fusion ( Link zur LV im LSF )
- Introduction to Distributed Sensor Data Fusion ( Link zur LV im LSF )
- Navigation in Aerial Robotics ( Link zur LV im LSF )
- Predictive Maintenance Seminar ( Link zur LV im LSF )