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[on SciFinder(R)]", "issn" : "0009-2614", "doi" : "10.1016/0009-2614(85)85405-1", "bibtexKey": "Kaim.1985g" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/237043d0c795fb6415f42c3cbc7821139/inue", "tags" : [ "localization","myown","from:sdnr","ml" ], "intraHash" : "37043d0c795fb6415f42c3cbc7821139", "interHash" : "9e12fc3098e3ba0e029e11d959b7c620", "label" : "On Deep Learning-Based Massive MIMO Indoor User Localization", "user" : "inue", "description" : "", "date" : "2022-03-22 12:24:26", "changeDate" : "2022-03-22 11:24:26", "count" : 3, "pub-type": "inproceedings", "booktitle": "2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8446013", "author": [ "Maximilian Arnold","Sebastian Dörner","Sebastian Cammerer","Stephan ten Brink" ], "authors": [ {"first" : "Maximilian", "last" : "Arnold"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Sebastian", "last" : "Cammerer"}, {"first" : "Stephan", "last" : "ten Brink"} ], "pages": "1-5","abstract": "We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a two-step training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.", "issn" : "1948-3252", "doi" : "10.1109/SPAWC.2018.8446013", "bibtexKey": "positioning_spawc" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2610dffb2ee56e926e84dc38d72403650/inue", "tags" : [ "localization","myown","from:sdnr","ml" ], "intraHash" : "610dffb2ee56e926e84dc38d72403650", "interHash" : "335beb417e4f5878b61eae57e246d5f5", "label" : "Towards Practical Indoor Positioning Based on Massive MIMO Systems", "user" : "inue", "description" : "", "date" : "2022-03-22 12:19:34", "changeDate" : "2022-03-22 11:19:34", "count" : 5, "pub-type": "inproceedings", "booktitle": "2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)", "year": "2019", "url": "https://ieeexplore.ieee.org/document/8891273", "author": [ "Mark Widmaier","Maximilian Arnold","Sebastian Dörner","Sebastian Cammerer","Stephan ten Brink" ], "authors": [ {"first" : "Mark", "last" : "Widmaier"}, {"first" : "Maximilian", "last" : "Arnold"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Sebastian", "last" : "Cammerer"}, {"first" : "Stephan", "last" : "ten Brink"} ], "pages": "1-6","abstract": "We showcase the practicability of an indoor positioning system (IPS) solely based on neural networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i.e., only build on the basis of data that is already existent in today's systems. As such our IPS system promises both, a good accuracy without the need of any additional protocol/signaling overhead for the user localization task. In particular, we propose a tailored NN structure with an additional phase branch as feature extractor and (compared to previous results) a significantly reduced amount of trainable parameters, leading to a minimization of the amount of required training data. We provide actual measurements for indoor scenarios with up to 64 antennas covering a large area of 80m 2 . In the second part, several robustness investigations for real-measurements are conducted, i.e., once trained, we analyze the recall accuracy over a period of several days. Further, we analyze the impact of pedestrians walking in-between the measurements and show that finetuning and pre-training of the NN helps to mitigate effects of hardware drifts and alterations in the propagation environment over time. This reduces the amount of required training samples at equal precision and, thereby, decreases the effort of the costly training data acquisition.", "issn" : "1090-3038", "doi" : "10.1109/VTCFall.2019.8891273", "bibtexKey": "positioning_2" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/250436676951b54922b2f4cc24abc8ed6/annettegugel", "tags" : [ "Cable","Contained","Evaluation","Frequency","Information","Localization","Monitoringsend:unibiblio","PD","Power","Signals","for","in","of","the" ], "intraHash" : "50436676951b54922b2f4cc24abc8ed6", "interHash" : "db9c8166528dee76bed04109853be2d0", "label" : "Evaluation of the Frequency Information Contained in PD Signals for Localization in Power Cable Monitoring", "user" : "annettegugel", "description" : "", "date" : "2021-01-29 12:33:34", "changeDate" : "2021-01-29 11:33:34", "count" : 1, "pub-type": "conference", "year": "2020", "url": "", "author": [ "Daniel Passow","Michael Beltle","Stefan Tenbohlen","Jens Hohloch","Martin Siegel" ], "authors": [ {"first" : "Daniel", "last" : "Passow"}, {"first" : "Michael", "last" : "Beltle"}, {"first" : "Stefan", "last" : "Tenbohlen"}, {"first" : "Jens", "last" : "Hohloch"}, {"first" : "Martin", "last" : "Siegel"} ], "pages": "288-293", "eventtitle" : "VDE Hochspannungstechnik 2020", "venue" : "Berlin, Germany, online", "eventdate" : "November 9-11", "bibtexKey": "noauthororeditor2020evaluation" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2dd9da33fb74d0e4af6fab07aa613e805/isw-bibliothek", "tags" : [ "2020","EXC2075","SimTech","Soft-Tissue-Robotics","deformable","grk2198","hze","isw","localization","multibody","myown","xwk" ], "intraHash" : "dd9da33fb74d0e4af6fab07aa613e805", "interHash" : "8618cb778197cd5895bf2391ac560030", "label" : "Kinematic Multibody Model Generation of Deformable Linear Objects from Point Clouds", "user" : "isw-bibliothek", "description" : "", "date" : "2021-01-21 11:45:33", "changeDate" : "2021-12-09 12:25:29", "count" : 3, "pub-type": "inproceedings", "booktitle": "2020 International Conference on Intelligent Robots and Systems (IROS)", "year": "2020", "url": "", "author": [ "Markus Wnuk","Christoph Hinze","Armin Lechler","Alexander Verl" ], "authors": [ {"first" : "Markus", "last" : "Wnuk"}, {"first" : "Christoph", "last" : "Hinze"}, {"first" : "Armin", "last" : "Lechler"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "9545-9552", "doi" : "10.1109/IROS45743.2020.9340887", "bibtexKey": "wnuk2020kinematic" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/24e004b357644fb2726a33e94f7b9c312/annettegugel", "tags" : [ "Acoustic","PD","algorithms","approach","direct","equations","iterative","localization","measurements","non-linear","observation","online","pseudo-time","solution","solver","strategies","time-differences" ], "intraHash" : "4e004b357644fb2726a33e94f7b9c312", "interHash" : "92f5da3aea1319b1a76568f9e1133e77", "label" : "All-Acoustic PD measurements of oil/paper-insulated transformers for PD-localization", "user" : "annettegugel", "description" : "", "date" : "2020-08-11 13:06:43", "changeDate" : "2020-08-11 11:06:43", "count" : 1, "pub-type": "proceedings", "year": "2004", "url": "https://www.ieh.uni-stuttgart.de/dokumente/publikationen/2004_APTADM_Markalous.pdf", "author": [ "Sacha M. 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Kristiansen" ], "authors": [ {"first" : "Martin", "last" : "Siegel"}, {"first" : "Stefan", "last" : "Tenbohlen"}, {"first" : "H.O.", "last" : "Kristiansen"} ], "eventtitle" : "18th International Symposium on High Voltage Engineering, ISH 2013", "venue" : "Seoul, South Korea", "eventdate" : "August 25-30", "bibtexKey": "siegel2013methods" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/256e7b3882ac550bf716e78ab2380b46a/annettegugel", "tags" : [ "Cables","Length","Localization","Long","PD","Power" ], "intraHash" : "56e7b3882ac550bf716e78ab2380b46a", "interHash" : "f428eb5806939c839740536231ab0816", "label" : "Practical Aspects of PD Localization for Long Length Power Cables", "user" : "annettegugel", "description" : "", "date" : "2020-05-12 15:14:15", "changeDate" : "2020-05-12 13:14:15", "count" : 1, "pub-type": "conference", "year": "2013", "url": "https://www.ieh.uni-stuttgart.de/dokumente/publikationen/2013_Wild_EIC_Practical_aspects_of_PD_localization...pdf", "author": [ "Manuel Wild","Stefan Tenbohlen","Edward Gulski","Rogier Jongen","F. de Vries" ], "authors": [ {"first" : "Manuel", "last" : "Wild"}, {"first" : "Stefan", "last" : "Tenbohlen"}, {"first" : "Edward", "last" : "Gulski"}, {"first" : "Rogier", "last" : "Jongen"}, {"first" : "F.", "last" : "de Vries"} ], "eventtitle" : "Electrical Insulation Conference 2013 (EIC)", "venue" : "Ottawa, Canada", "eventdate" : "June 2-5", "bibtexKey": "wild2013practical" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a76ca2f1d096a8c6b9fec9442f25ad16/inue", "tags" : [ "extractor;Artificial","multiple-output","myown","from:sdnr","(artificial","communication;neural","nets;telecommunication","system;neural","extraction","arrays;Feature","structure;phase","measurements;Training;Antenna","radio;learning","system;user","intelligence);MIMO","acquisition;feature","NN","localization","communication;indoor","neural","data","networks;channel","branch;indoor","extraction;indoor","systems;indoor","scenarios;recall","positioning","MIMO","accuracy;training","networks;Antenna","information;multiple-input","state","system;IPS","computing;massive","task;tailored","communication" ], "intraHash" : "a76ca2f1d096a8c6b9fec9442f25ad16", "interHash" : "335beb417e4f5878b61eae57e246d5f5", "label" : "Towards Practical Indoor Positioning Based on Massive MIMO Systems", "user" : "inue", "description" : "", "date" : "2020-03-20 15:07:06", "changeDate" : "2020-03-20 14:07:06", "count" : 5, "pub-type": "inproceedings", "booktitle": "2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)", "year": "2019", "url": "", "author": [ "M. Widmaier","M. Arnold","S. Dörner","S. Cammerer","S. ten Brink" ], "authors": [ {"first" : "M.", "last" : "Widmaier"}, {"first" : "M.", "last" : "Arnold"}, {"first" : "S.", "last" : "Dörner"}, {"first" : "S.", "last" : "Cammerer"}, {"first" : "S.", "last" : "ten Brink"} ], "pages": "1-6", "issn" : "1090-3038", "doi" : "10.1109/VTCFall.2019.8891273", "bibtexKey": "positioning_2" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/237043d0c795fb6415f42c3cbc7821139/sdnr", "tags" : [ "localization","ml","myown" ], "intraHash" : "37043d0c795fb6415f42c3cbc7821139", "interHash" : "9e12fc3098e3ba0e029e11d959b7c620", "label" : "On Deep Learning-Based Massive MIMO Indoor User Localization", "user" : "sdnr", "description" : "", "date" : "2020-03-19 13:20:38", "changeDate" : "2022-03-22 11:24:26", "count" : 3, "pub-type": "inproceedings", "booktitle": "2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8446013", "author": [ "Maximilian Arnold","Sebastian Dörner","Sebastian Cammerer","Stephan ten Brink" ], "authors": [ {"first" : "Maximilian", "last" : "Arnold"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Sebastian", "last" : "Cammerer"}, {"first" : "Stephan", "last" : "ten Brink"} ], "pages": "1-5","abstract": "We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a two-step training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.", "issn" : "1948-3252", "doi" : "10.1109/SPAWC.2018.8446013", "bibtexKey": "positioning_spawc" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2610dffb2ee56e926e84dc38d72403650/sdnr", "tags" : [ "localization","ml","myown" ], "intraHash" : "610dffb2ee56e926e84dc38d72403650", "interHash" : "335beb417e4f5878b61eae57e246d5f5", "label" : "Towards Practical Indoor Positioning Based on Massive MIMO Systems", "user" : "sdnr", "description" : "", "date" : "2020-03-19 13:20:38", "changeDate" : "2022-03-22 11:19:34", "count" : 5, "pub-type": "inproceedings", "booktitle": "2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)", "year": "2019", "url": "https://ieeexplore.ieee.org/document/8891273", "author": [ "Mark Widmaier","Maximilian Arnold","Sebastian Dörner","Sebastian Cammerer","Stephan ten Brink" ], "authors": [ {"first" : "Mark", "last" : "Widmaier"}, {"first" : "Maximilian", "last" : "Arnold"}, {"first" : "Sebastian", "last" : "Dörner"}, {"first" : "Sebastian", "last" : "Cammerer"}, {"first" : "Stephan", "last" : "ten Brink"} ], "pages": "1-6","abstract": "We showcase the practicability of an indoor positioning system (IPS) solely based on neural networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i.e., only build on the basis of data that is already existent in today's systems. As such our IPS system promises both, a good accuracy without the need of any additional protocol/signaling overhead for the user localization task. In particular, we propose a tailored NN structure with an additional phase branch as feature extractor and (compared to previous results) a significantly reduced amount of trainable parameters, leading to a minimization of the amount of required training data. We provide actual measurements for indoor scenarios with up to 64 antennas covering a large area of 80m 2 . In the second part, several robustness investigations for real-measurements are conducted, i.e., once trained, we analyze the recall accuracy over a period of several days. Further, we analyze the impact of pedestrians walking in-between the measurements and show that finetuning and pre-training of the NN helps to mitigate effects of hardware drifts and alterations in the propagation environment over time. This reduces the amount of required training samples at equal precision and, thereby, decreases the effort of the costly training data acquisition.", "issn" : "1090-3038", "doi" : "10.1109/VTCFall.2019.8891273", "bibtexKey": "positioning_2" } ] }