@inproceedings{EbnerFetzerBullmannetal., author = {Ebner, Markus and Fetzer, Toni and Bullmann, Markus and Kastner, Steffen and Deinzer, Frank and Grzegorzek, Marcin}, title = {PIPF: Proposal-Interpolating Particle Filter}, series = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2022)}, booktitle = {International Conference on Indoor Positioning and Indoor Navigation (IPIN 2022)}, language = {en} } @article{KastnerEbnerBullmannetal., author = {Kastner, Steffen and Ebner, Markus and Bullmann, Markus and Fetzer, Toni and Deinzer, Frank and Grzegorzek, Marcin}, title = {Magnetic Signature Sensor Model for Accurate Short-Distance Localization}, series = {2022 IEEE Sensors}, journal = {2022 IEEE Sensors}, doi = {10.1109/SENSORS52175.2022.9967176}, pages = {1 -- 4}, language = {en} } @article{FetzerMaierEbneretal., author = {Fetzer, Toni and Maier, Julian and Ebner, Markus and Bullmann, Markus and Deinzer, Frank}, title = {Digitales Spaghetti-Diagramm zur Laufweganalyse}, series = {wt Werkstattstechnik online}, volume = {112}, journal = {wt Werkstattstechnik online}, number = {10/2022}, pages = {727 -- 731}, language = {de} } @article{BullmannFetzerEbneretal., author = {Bullmann, Markus and Fetzer, Toni and Ebner, Frank and Ebner, Markus and Deinzer, Frank and Grzegorzek, Marcin}, title = {Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {16}, issn = {1424-8220}, doi = {10.3390/s20164515}, language = {en} } @inproceedings{KastnerBullmannEbneretal., author = {Kastner, Steffen and Bullmann, Markus and Ebner, Markus and Fetzer, Toni and Deinzer, Frank and Grzegorzek, Marcin}, title = {Refinement of Sparsely Tagged Ground Truth Paths Using PDR and Particle Filter Smoothing}, series = {2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, booktitle = {2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IPIN62893.2024.10786186}, pages = {1 -- 6}, language = {en} } @inproceedings{KastnerEbnerBullmannetal., author = {Kastner, Steffen and Ebner, Markus and Bullmann, Markus and Fetzer, Toni and Deinzer, Frank and Grzegorzek, Marcin}, title = {SIMUL: Synchronized IMU Dataset of Walking People at Six Body Locations}, series = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, booktitle = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IPIN57070.2023.10332491}, pages = {1 -- 7}, language = {en} } @inproceedings{FetzerBullmannEbneretal., author = {Fetzer, Toni and Bullmann, Markus and Ebner, Markus and Kastner, Steffen and Deinzer, Frank and Grzegorzek, Marcin}, title = {Interacting Multiple Model Particle Filter for Indoor Positioning Applications}, series = {Proceedings of the 2023 International Technical Meeting of The Institute of Navigation}, booktitle = {Proceedings of the 2023 International Technical Meeting of The Institute of Navigation}, language = {en} } @article{WernerBullmannFetzeretal., author = {Werner, Max and Bullmann, Markus and Fetzer, Toni and Deinzer, Frank}, title = {Unified Probabilistic and Similarity-Based Position Estimation from Radio Observations}, series = {Sensors}, volume = {25}, journal = {Sensors}, number = {13}, publisher = {MDPI AG}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s25134092}, abstract = {We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations.}, language = {en} } @inproceedings{WernerBullmannFetzeretal., author = {Werner, Max and Bullmann, Markus and Fetzer, Toni and Meißner, Pascal and Deinzer, Frank}, title = {Interpolation of Position Estimates for Radio Fingerprinting using Gaussian Process Regression}, series = {2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, booktitle = {2025 International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, publisher = {IEEE}, doi = {10.1109/IPIN66788.2025.11213294}, pages = {1 -- 6}, language = {en} } @article{PrappacherBullmannBohnetal., author = {Prappacher, Nico and Bullmann, Markus and Bohn, Gunther and Deinzer, Frank and Linke, Andreas}, title = {Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation}, series = {Applied Sciences}, volume = {10}, journal = {Applied Sciences}, number = {9}, issn = {2076-3417}, doi = {10.3390/app10093290}, language = {en} } @inproceedings{BullmannFetzerEbneretal., author = {Bullmann, Markus and Fetzer, Toni and Ebner, Frank and Deinzer, Frank and Grzegorzek, Marcin}, title = {Fast Kernel Density Estimation Using Gaussian Filter Approximation}, series = {21st International Conference on Information Fusion, FUSION 2018, Cambridge, UK, July 10-13, 2018}, booktitle = {21st International Conference on Information Fusion, FUSION 2018, Cambridge, UK, July 10-13, 2018}, doi = {10.23919/ICIF.2018.8455686}, pages = {1233 -- 1240}, language = {en} } @article{FetzerEbnerBullmannetal., author = {Fetzer, Toni and Ebner, Frank and Bullmann, Markus and Deinzer, Frank and Grzegorzek, Marcin}, title = {Smartphone-Based Indoor Localization within a 13th Century Historic Building}, series = {Sensors}, volume = {18}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s18124095}, language = {en} } @inproceedings{FetzerBullmannKastneretal., author = {Fetzer, Toni and Bullmann, Markus and Kastner, Steffen and Deinzer, Frank and Grzegorzek, Marcin}, title = {Advancing Smartphone-based Indoor Positioning through Particle Distribution Optimization}, series = {2024 27th International Conference on Information Fusion (FUSION)}, booktitle = {2024 27th International Conference on Information Fusion (FUSION)}, publisher = {IEEE}, doi = {https://doi.org/10.23919/FUSION59988.2024.10706408}, pages = {1 -- 8}, language = {en} }