CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions. In this paper, we propose a CSI-based positioning pipeline for wireless LAN MIMO-OFDM systems operating indoors, which relies on NNs that extract a probability map indicating the likelihood of a UE being at a given grid point. We propose methods to fuse these probability maps at a centralized processor, which enables improved positioning accuracy if CSI is acquired at different access points (APs) and extracted from different transmit antennas. To improve positioning accuracy, we propose the design of CSI features that are robust to hardware and system impairments arising in real-world MIMO-OFDM transceivers. We provide experimental results with real-world indoor measurements under line-of-sight (LoS) and non-LoS propagation conditions, and for multi-antenna and multi-AP measurements. Our results demonstrate that probability fusion significantly improves positioning accuracy without requiring exact synchronization between APs and that centimeter-level median distance error is achievable.

[1]  Olav Tirkkonen,et al.  Unsupervised Charting of Wireless Channels , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[2]  Slawomir Stanczak,et al.  Channel Charting: an Euclidean Distance Matrix Completion Perspective , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[4]  Shih-Hau Fang,et al.  A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments , 2008, IEEE Transactions on Wireless Communications.

[5]  Shiwen Mao,et al.  BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi , 2017, IEEE Access.

[6]  Xiao Fu,et al.  Survey on CSI-based Indoor Positioning Systems and Recent Advances , 2019, 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[7]  Stephan ten Brink,et al.  Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning , 2018, 1810.04126.

[8]  Dou Long,et al.  Fusion of detection probabilities and comparison of multisensor systems , 1990, IEEE Trans. Syst. Man Cybern..

[9]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[10]  Olav Tirkkonen,et al.  Channel Charting: Locating Users Within the Radio Environment Using Channel State Information , 2018, IEEE Access.

[11]  Shaoqian Li,et al.  CSI Based High Accuracy Device Free Passive Localization System , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[12]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[13]  Olav Tirkkonen,et al.  Multipoint Channel Charting for Wireless Networks , 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

[14]  Alexandre Caminada,et al.  DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context , 2018, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[15]  Geoffrey Ye Li,et al.  Fingerprint-Based Localization for Massive MIMO-OFDM System With Deep Convolutional Neural Networks , 2019, IEEE Transactions on Vehicular Technology.

[16]  Di He,et al.  2D DOA estimation method based on channel state information for uniform circular array , 2016, 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS).

[17]  Moshe Kam,et al.  Evidence combination for hard and soft sensor data fusion , 2011, 14th International Conference on Information Fusion.

[18]  Christoph Studer,et al.  Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing , 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

[19]  Ramón F. Brena,et al.  Evolution of Indoor Positioning Technologies: A Survey , 2017, J. Sensors.

[20]  Alexis Decurninge,et al.  Triplet-Based Wireless Channel Charting , 2020, ArXiv.

[21]  Kaishun Wu,et al.  CSI-Based Indoor Localization , 2013, IEEE Transactions on Parallel and Distributed Systems.

[22]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[23]  Erik G. Larsson,et al.  Direct Localization for Massive MIMO , 2016, IEEE Transactions on Signal Processing.

[24]  Stephan ten Brink,et al.  Towards Practical Indoor Positioning Based on Massive MIMO Systems , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[25]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[26]  Shuang-Hua Yang,et al.  A Survey of Indoor Positioning and Object Locating Systems , 2010 .

[27]  Mérouane Debbah,et al.  Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? , 2019, IEEE Transactions on Communications.

[28]  Sanjay Jha,et al.  CSI-MIMO: Indoor Wi-Fi fingerprinting system , 2014, 39th Annual IEEE Conference on Local Computer Networks.

[29]  Yunhao Liu,et al.  From RSSI to CSI , 2013, ACM Comput. Surv..

[30]  Tim Schenk,et al.  RF Imperfections in High-rate Wireless Systems: Impact and Digital Compensation , 2008 .

[31]  Yongsen Ma,et al.  WiFi Sensing with Channel State Information , 2019, ACM Comput. Surv..

[32]  Prabal Dutta,et al.  Luxapose: indoor positioning with mobile phones and visible light , 2014, MobiCom.

[33]  Michael D. Zoltowski,et al.  Closed-form 3D angle estimation with rectangular arrays via DFT Beamspace ESPRIT , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[34]  Cheng Li,et al.  Future Alternative Positioning, Navigation, and Timing Techniques: A Survey , 2016, IEEE Wireless Communications.

[35]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[36]  Tom Goldstein,et al.  Improving Channel Charting with Representation -Constrained Autoencoders , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[37]  Akbar M. Sayeed,et al.  Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements , 2013, IEEE Transactions on Antennas and Propagation.

[38]  Chunhua Geng,et al.  Multipoint Channel Charting With Multiple-Input Multiple-Output Convolutional Autoencoder , 2020, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[39]  Torsten Bertram,et al.  Object existence probability fusion using dempster-shafer theory in a high-level sensor data fusion architecture , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[40]  Theodore P. Hill,et al.  Conflations of probability distributions , 2008, 0808.1808.

[41]  Kostas E. Bekris,et al.  Indoor Human Navigation Systems: A Survey , 2013, Interact. Comput..

[42]  Chunhan Lee,et al.  Indoor positioning system based on incident angles of infrared emitters , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[43]  Adrian Neild,et al.  Visible light positioning: a roadmap for international standardization , 2013, IEEE Commun. Mag..

[44]  Andreas Peter Burg,et al.  FFT Processor for OFDM Channel Estimation , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[45]  Andreas Peter Burg,et al.  MIMO transmission with residual transmit-RF impairments , 2010, 2010 International ITG Workshop on Smart Antennas (WSA).

[46]  H. H. Rachford,et al.  On the numerical solution of heat conduction problems in two and three space variables , 1956 .

[47]  Tom Goldstein,et al.  Siamese Neural Networks for Wireless Positioning and Channel Charting , 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[48]  Fredrik Tufvesson,et al.  Deep convolutional neural networks for massive MIMO fingerprint-based positioning , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[49]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

[50]  Henk Wymeersch,et al.  A survey on 5G massive MIMO localization , 2019, Digit. Signal Process..

[51]  Erik G. Larsson,et al.  Fingerprinting-Based Positioning in Distributed Massive MIMO Systems , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[52]  Alexis Decurninge,et al.  DNN-based Localization from Channel Estimates: Feature Design and Experimental Results , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[53]  Swarun Kumar,et al.  Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards , 2015, SIGCOMM.

[54]  Danyang Li,et al.  Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition , 2017, Cognitive Computation.

[55]  Chuan Zhang,et al.  Artificial Intelligence for 5G and Beyond 5G: Implementations, Algorithms, and Optimizations , 2020, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[56]  Hao Chen,et al.  ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information , 2017, IEEE Access.