{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:43:44Z","timestamp":1774021424351,"version":"3.50.1"},"reference-count":83,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T00:00:00Z","timestamp":1551484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This work was supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics - Data - Applications (ADA-Center) within the framework of \u201eBAYERN DIGITAL II\u201c","award":["."],"award-info":[{"award-number":["."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.<\/jats:p>","DOI":"10.3390\/s19051064","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:45:36Z","timestamp":1551678336000},"page":"1064","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["A Deep Learning Approach to Position Estimation from Channel Impulse Responses"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1493-0367","authenticated-orcid":false,"given":"Arne","family":"Niitsoo","sequence":"first","affiliation":[{"name":"Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 N\u00fcrnberg, Germany"}]},{"given":"Thorsten","family":"Edelh\u00e4u\u00dfer","sequence":"additional","affiliation":[{"name":"Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 N\u00fcrnberg, Germany"}]},{"given":"Ernst","family":"Eberlein","sequence":"additional","affiliation":[{"name":"Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 N\u00fcrnberg, Germany"}]},{"given":"Niels","family":"Hadaschik","sequence":"additional","affiliation":[{"name":"Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 N\u00fcrnberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8108-0230","authenticated-orcid":false,"given":"Christopher","family":"Mutschler","sequence":"additional","affiliation":[{"name":"Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 N\u00fcrnberg, Germany"},{"name":"Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg (FAU), Carl-Thiersch-Stra\u00dfe 2b, 91052 Erlangen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Niitsoo, A., Edelh\u00e4u\u00dfer, T., and Mutschler, C. (2018, January 24\u201327). Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533766"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gradl, S., Eskofier, B.M., Eskofier, D., Mutschler, C., and Otto, S. (2016, January 12\u201316). Virtual and augmented reality in sports: An overview and acceptance study. Proceedings of the 2016 ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp Adjunct 2016, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968572"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Feigl, T., Mutschler, C., and Philippsen, M. (2018, January 24\u201327). Supervised Learning for Yaw Orientation Estimation. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533811"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roth, D., Kleinbeck, C., Feigl, T., Mutschler, C., and Latoschik, M.E. (2018, January 18\u201322). Beyond Replication: Augmenting Social Behaviors in Multi-User Virtual Realities. Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces, Reutlingen, Germany.","DOI":"10.1109\/VR.2018.8447550"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1109\/TIM.2017.2681398","article-title":"Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis","volume":"66","author":"Ruiz","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1017\/S1759078711000274","article-title":"Dynamic Multipath Mitigation applying Unscented Kalman Filters in Local Positioning Systems","volume":"3","author":"Nowak","year":"2011","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_7","unstructured":"Zhang, C., Bao, X., Wei, Q., Ma, Q., Yang, Y., and Wang, Q. (2016, January 2\u20134). A Kalman filter for UWB positioning in LOS\/NLOS scenarios. Proceedings of the 4th International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services, Shanghai, China."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3766","DOI":"10.1109\/JSEN.2014.2328353","article-title":"CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment","volume":"14","author":"He","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Exel, R., and Bigler, T. (2014, January 6\u20139). ToA Ranging using Subsample Peak Estimation and Equalizer-based Multipath Reduction. Proceedings of the IEEE Wireless Communications and Networking Conference, Istanbul, Turkey.","DOI":"10.1109\/WCNC.2014.6952928"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Driusso, M., Babich, F., Knutti, F., Sabathy, M., and Marshall, C. (2015, January 7\u20139). Estimation and Tracking of LTE signals Time of Arrival in a Mobile Multipath Environment. Proceedings of the 9th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia.","DOI":"10.1109\/ISPA.2015.7306072"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jin, B., Xu, X., and Zhang, T. (2016, January 7\u201311). A Fast Location Algorithm Based on TDOA. Proceedings of the 4th International Conference on Control, Mechatronics and Automation, Barcelona, Spain.","DOI":"10.1145\/3029610.3029630"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"778","DOI":"10.3390\/s18030778","article-title":"Robust Time-Difference-of-Arrival (TDOA) Localization Using Weighted Least Squares with Cone Tangent Plane Constraint","volume":"18","author":"Jin","year":"2018","journal-title":"Sensors"},{"key":"ref_13","unstructured":"Al-Jazzar, S., Caffery, J., and You, H.R. (2002, January 6\u20139). A Scattering Model based Approach to NLOS Mitigation in TOA Location Systems. Proceedings of the 55th IEEE Conference on Vehicular Technology, Birmingham, AL, USA."},{"key":"ref_14","unstructured":"Al-Jazzar, S., and Caffery, J. (2002, January 24\u201328). ML and Bayesian TOA location estimators for NLOS environments. Proceedings of the 56th IEEE Conference on Vehicular Technology, Vancouver, BC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/JSTSP.2013.2289949","article-title":"Simultaneous Target and Multipath Positioning","volume":"8","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3996","DOI":"10.1109\/JSEN.2014.2356857","article-title":"Toward Accurate Human Tracking: Modeling Time-of-Arrival for Wireless Wearable Sensors in Multipath Environment","volume":"14","author":"He","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/LWC.2014.2341636","article-title":"UWB for Robust Indoor Tracking: Weighting of Multipath Components for Efficient Estimation","volume":"3","author":"Meissner","year":"2014","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1109\/TSP.2017.2666779","article-title":"Direct Localization for Massive MIMO","volume":"65","author":"Garcia","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kendall, A., Grimes, M., and Cipolla, R. (2015, January 7\u201313). PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization. Proceedings of the 2015 International Conference on Computer Vision, Santiago de Chile, Chile.","DOI":"10.1109\/ICCV.2015.336"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mascharka, D., and Manley, E. (2016, January 9\u201312). LIPS: Learning Based Indoor Positioning System using mobile phone-based sensors. Proceedings of the 13th IEEE Annual Consumer Communications and Networking Conference, Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2016.7444919"},{"key":"ref_21","unstructured":"Martinez Sala, A., Quir\u2019os, R., and L\u2019opez, E. (2010, January 15\u201316). Using neural networks and Active RFID for indoor location services. Proceedings of the European Workshop Smart Objects: Systems, Technologies and Applications, Ciudad, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/5840916","article-title":"Deep Belief Networks for Fingerprinting Indoor Localization Using Ultrawideband Technology","volume":"12","author":"Luo","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8402","DOI":"10.1109\/TWC.2018.2876832","article-title":"Machine Learning Methods for RSS-Based User Positioning in Distributed Massive MIMO","volume":"17","author":"Prasad","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18431","DOI":"10.1109\/ACCESS.2018.2805841","article-title":"Analytical Approximation-Based Machine Learning Methods for User Positioning in Distributed Massive MIMO","volume":"6","author":"Prasad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Vaghefi, S.Y.M., and Vaghefi, R.M. (August, January 31). A Novel Multilayer Network Model for TOA-Based Localization in Wireless Sensor Networks. Proceedings of the International Joint Conference on Neural Networks, San Jose, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Singh, P., and Agrawal, S. (2013, January 6\u20138). TDOA Based Node Localization in WSN using Neural Networks. Proceedings of the International Conference on Communication Systems and Network Technologies, Gwalior, India.","DOI":"10.1109\/CSNT.2013.90"},{"key":"ref_27","unstructured":"Lewandowski, A., K\u00f6ster, V., Wietfeld, C., and Michaelis, S. (2011, January 24\u201326). Support Vector Machines for Non-Linear Radio Fingerprint Recognition in Real-Life Industrial Environments. Proceedings of the International Conference on Technical Meeting, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.3390\/s120302798","article-title":"Artificial Neural Network for Location Estimation in Wireless Communication Systems","volume":"12","author":"Chen","year":"2012","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Le, D.V., Meratnia, N., and Havinga, P.J.M. (2018, January 24\u201327). Unsupervised Deep Feature Learning to Reduce the Collection of Fingerprints for Indoor Localization Using Deep Belief Networks. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533790"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"F\u00e9lix, G., Siller, M., and \u00c1lvarez, E.N. (2016, January 5\u20138). A Fingerprinting Indoor Localization Algorithm based Deep Learning. Proceedings of the 8th International Conference on Ubiquitous and Future Networks, Vienna, Austria.","DOI":"10.1109\/ICUFN.2016.7536949"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s41044-018-0031-2","article-title":"A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization based on Wi-Fi Fingerprinting","volume":"3","author":"Kim","year":"2018","journal-title":"Big Data Anal."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.cie.2012.06.006","article-title":"Application of an Artificial Immune System-based Fuzzy Neural Network to a RFID-based Positioning System","volume":"63","author":"Kuo","year":"2012","journal-title":"J. Comput. Ind. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Savic, V., and Larsson, E.G. (2015, January 6\u20139). Fingerprinting-Based Positioning in Distributed Massive MIMO Systems. Proceedings of the 82nd IEEE Conference on Vehicular Technology, Boston, MA, USA.","DOI":"10.1109\/VTCFall.2015.7390953"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"38251","DOI":"10.1109\/ACCESS.2018.2852658","article-title":"HybLoc: Hybrid Indoor Wi-Fi Localization Using Soft Clustering-Based Random Decision Forest Ensembles","volume":"6","author":"Akram","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Iqbal, Z., Luo, D., Henry, P., Kazemifar, S., Rozario, T., Yan, Y., Westover, K., Lu, W., Nguyen, D., and Long, T. (2017). Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205392"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ibrahim, M., Torki, M., and ElNainay, M. (2018, January 25\u201328). CNN based Indoor Localization using RSS Time-Series. Proceedings of the 2018 IEEE Symposium on Computers and Communications, Natal, Brazil.","DOI":"10.1109\/ISCC.2018.8538530"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sahar, A., and Han, D. (2018, January 27\u201329). An LSTM-based Indoor Positioning Method Using Wi-Fi Signals. Proceedings of the 2nd International Conference on Vision, Image and Signal Processing, Las Vegas, NV, USA.","DOI":"10.1145\/3271553.3271566"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Feigl, T., Nowak, T., Philippsen, M., Edelh\u00e4u\u00dfer, T., and Mutschler, C. (2018, January 24\u201327). Recurrent Neural Networks on Drifting Time-of-Flight Measurements. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533813"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/JIOT.2017.2712560","article-title":"Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services","volume":"5","author":"Mohammadi","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12071","DOI":"10.1109\/ACCESS.2017.2712131","article-title":"Neural-Network-Assisted UE Localization Using Radio-Channel Fingerprints in LTE Networks","volume":"5","author":"Ye","year":"2017","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, Y., Tseng, P., Chan, Y., He, J., and Wu, G. (2018). A Super-resolution-assisted Fingerprinting Method based on Channel Impulse Response Measurement for Indoor Positioning. IEEE Trans. Mob. Comput.","DOI":"10.1109\/WCNC.2017.7925866"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yu, L., Laaraiedh, M., Avrillon, S., and Uguen, B. (2011, January 14\u201317). Fingerprinting localization based on neural networks and ultra-wideband signals. Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, Bilbao, Spain.","DOI":"10.1109\/ISSPIT.2011.6151557"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hong, A.N., Rath, M., Kulmer, J., Grebien, S., Van, K.N., and Witrisal, K. (2018, January 18\u201320). Gaussian Process Modeling of UWB Multipath Components. Proceedings of the 2018 IEEE 7th International Conference on Communications and Electronics, Hue, Vietnam.","DOI":"10.1109\/CCE.2018.8465704"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/JSAC.2010.100907","article-title":"NLOS Identification and Mitigation for Localization based on UWB Experimental Data","volume":"28","author":"Marano","year":"2010","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_45","unstructured":"Li, W., Zhang, T., and Zhang, Q. (2013, January 17\u201319). Experimental researches on an UWB NLOS Identification Method based on Machine learning. Proceedings of the 15th IEEE International Conference on Communication Technology, Guilin, China."},{"key":"ref_46","unstructured":"De Reyna, E.A., Dardari, D., Closas, P., and Djuric, P.M. (2018, January 10\u201313). Estimation of Spatial Fields of Nlos\/Los Conditions for Improved Localization in Indoor Environments. Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, Freiburg, Germany."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3295","DOI":"10.1109\/TVT.2017.2780121","article-title":"Deep Learning Based NLOS Identification With Commodity WLAN Devices","volume":"67","author":"Choi","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"17429","DOI":"10.1109\/ACCESS.2018.2817800","article-title":"Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices","volume":"6","author":"Bregar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_49","first-page":"1311","article-title":"Threshold Selection for Ultra-Wideband TOA Estimation based on Neural Networks","volume":"7","author":"Cui","year":"2012","journal-title":"J. Netw."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1109\/TWC.2015.2496584","article-title":"Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity","volume":"15","author":"Savic","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Erg\u00fct, S., Rao, R., Dural, O., and Sahinoglu, Z. (2008, January 19\u201323). Localization via TDOA in a UWB sensor network using Neural Networks. Proceedings of the International Conference on Communications, Beijing, China.","DOI":"10.1109\/ICC.2008.456"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1109\/TWC.2010.03.090197","article-title":"Indoor Localization with Channel Impulse Response based Fingerprint and Nonparametric Regression","volume":"9","author":"Jin","year":"2010","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ghourchian, N., Allegue-Martinez, M., and Precup, D. (2017, January 4\u20137). Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i2.19093"},{"key":"ref_54","first-page":"763","article-title":"CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/JIOT.2016.2558659","article-title":"CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach","volume":"3","author":"Wang","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, X., and Mao, S. (2017, January 21\u201325). CiFi: Deep Convolutional Neural Networks for Indoor Localization with 5 GHz Wi-Fi. Proceedings of the IEEE International Conference on Communications, Paris, France.","DOI":"10.1109\/ICC.2017.7997235"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1109\/ACCESS.2017.2688362","article-title":"BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5 GHz WiFi","volume":"5","author":"Wang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Berruet, B., Baala, O., Caminada, A., and Guillet, V. (2018, January 24\u201327). DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533777"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"74699","DOI":"10.1109\/ACCESS.2018.2884193","article-title":"Indoor Positioning based on Dingerprint-Image and Deep Learning","volume":"6","author":"Shao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiu, C., Zhang, X., and Yang, D. (2018). WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest. Sensors, 18.","DOI":"10.3390\/s18092869"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wu, G., and Tseng, P. (2018, January 5\u20138). A Deep Neural Network-Based Indoor Positioning Method using Channel State Information. Proceedings of the International Conference on Computing, Networking and Communications, Maui, HI, USA.","DOI":"10.1109\/ICCNC.2018.8390298"},{"key":"ref_62","unstructured":"Yazdanian, P., and Pourahmadi, V. (arXiv, 2018). DeepPos: Deep Supervised Autoencoder Network for CSI Based Indoor Localization, arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2017.2787651","article-title":"A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine","volume":"2","author":"Khatab","year":"2018","journal-title":"IEEE Sens. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tiemann, J., Pillmann, J., and Wietfeld, C. (2017, January 4\u20137). Ultra-Wideband Antenna-Induced Error Prediction Using Deep Learning on Channel Response Data. Proceedings of the 85th Vehicular Technology Conference, Sydney, Australia.","DOI":"10.1109\/VTCSpring.2017.8108571"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Vieira, J., Leitinger, E., Sarajlic, M., Li, X., and Tufvesson, F. (2017, January 8\u201313). Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning. Proceedings of the 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292280"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Decurninge, A., Ord\u00f3\u00f1ez, L.G., Ferrand, P., He, G., Li, B., Zhang, W., and Guillaud, M. (2018, January 28\u201331). CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach. Proceedings of the 15th International Symposium on Wireless Communication Systems, Lisbon, Portugal.","DOI":"10.1109\/ISWCS.2018.8491210"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Arnold, M., Dorner, S., Cammerer, S., and Brink, S.T. (2018, January 25\u201328). On Deep Learning-Based Massive MIMO Indoor User Localization. Proceedings of the 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, Kalamata, Greece.","DOI":"10.1109\/SPAWC.2018.8446013"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"21","DOI":"10.5121\/ijcnc.2017.9302","article-title":"A Structured Deep Neural Network for Data Driven Localization in High Frequency Wireless Networks","volume":"9","author":"Comiter","year":"2017","journal-title":"Int. J. Comput. Netw. Commun."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Comiter, M.Z., and Kung, H. (2018, January 9\u201313). Localization Convolutional Neural Networks Using Angle of Arrival Images. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE.","DOI":"10.1109\/GLOCOM.2018.8647687"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"12751","DOI":"10.1109\/ACCESS.2017.2720164","article-title":"3-D BLE Indoor Localization Based on Denoising Autoencoder","volume":"5","author":"Xiao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_71","unstructured":"Guenter Hofmann, M.B. (2009). Device and Method for Determining a Time of Arrival of a Receive Sequence. (7,627,063), U.S. Patent."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mutschler, C., Ziekow, H., and Jerzak, Z. (2013, January 29). The DEBS 2013 Grand Challenge. Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems, Arlington, TX, USA.","DOI":"10.1145\/2488222.2488283"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Feigl, T., Mutschler, C., and Philippsen, M. (2018, January 18\u201322). Human Compensation Strategies for Orientation Drifts. Proceedings of the 25th IEEE International Conference on Virtual Reality and 3D User Interfaces, Reutlingen, Germany.","DOI":"10.1109\/VR.2018.8446300"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_76","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 24). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MSP.2012.2183773","article-title":"Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts","volume":"29","author":"Zoubir","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_78","unstructured":"Taylor, J.R. (1996). An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements, University Science Books. [2nd ed.]."},{"key":"ref_79","first-page":"38","article-title":"Peirce\u2019s criterion for the elimination of suspect experimental data","volume":"20","author":"Ross","year":"2003","journal-title":"J. Eng. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"L\u00f6ffler, C., Riechel, S., Fischer, J., and Mutschler, C. (2018, January 24\u201327). Evaluation Criteria for Inside-Out Indoor Positioning Systems Based on Machine Learning. Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, Nantes, France.","DOI":"10.1109\/IPIN.2018.8533862"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"31772","DOI":"10.1109\/ACCESS.2018.2838590","article-title":"An Indoor Localization Method Based on AOA and PDOA Using Virtual Stations in Multipath and NLOS Environments for Passive UHF RFID","volume":"6","author":"Ma","year":"2018","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ulmschneider, M., Luz, D.C., and Gentner, C. (2018, January 23\u201326). Exchanging transmitter maps in multipath assisted positioning. Proceedings of the IEEE\/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373480"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/JPROC.2018.2819638","article-title":"A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization","volume":"106","author":"Aditya","year":"2018","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1064\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:48Z","timestamp":1760186148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,2]]},"references-count":83,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["s19051064"],"URL":"https:\/\/doi.org\/10.3390\/s19051064","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,2]]}}}