Multi-Level Fusion Indoor Positioning Technology Considering Credible Evaluation Analysis

Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity.

[1]  Muhammad Junaid Tahir,et al.  RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization , 2022, Sensors.

[2]  R. Luo,et al.  Robust Indoor Localization Using Histogram of Oriented Depth Model Feature Map for Intelligent Service Robotics , 2022, IEEE/ASME Transactions on Mechatronics.

[3]  Lixing Wang,et al.  Overview of WiFi fingerprinting-based indoor positioning , 2022, IET Commun..

[4]  K. Abed-Meraim,et al.  A Survey of Magnetic-Field-Based Indoor Localization , 2022, Electronics.

[5]  Shehroz S. Khan,et al.  Indoor Location Data for Tracking Human Behaviours: A Scoping Review , 2022, Sensors.

[6]  Houcine Chafouk,et al.  A Survey of Recent Indoor Localization Scenarios and Methodologies , 2021, Sensors.

[7]  Olumide Alamu,et al.  An overview of massive MIMO localization techniques in wireless cellular networks: Recent advances and outlook , 2021, Ad Hoc Networks.

[8]  Jari Nurmi,et al.  Collaborative Indoor Positioning Systems: A Systematic Review , 2021, Sensors.

[9]  Baoguo Yu,et al.  HPIPS: A High-Precision Indoor Pedestrian Positioning System Fusing WiFi-RTT, MEMS, and Map Information , 2020, Sensors.

[10]  Ibraheem Shayea,et al.  An Overview of Indoor Localization Technologies: Toward IoT Navigation Services , 2020, 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT).

[11]  Hyoung-Gook Kim,et al.  Deep Neural Network-Based Indoor Emergency Awareness Using Contextual Information From Sound, Human Activity, and Indoor Position on Mobile Device , 2020, IEEE Transactions on Consumer Electronics.

[12]  Jack Chin Pang Cheng,et al.  Top 10 technologies for indoor positioning on construction sites , 2020 .

[13]  Fazirulhisyam Hashim,et al.  A three-dimensional pattern recognition localization system based on a Bayesian graphical model , 2020, Int. J. Distributed Sens. Networks.

[14]  Poompat Saengudomlert,et al.  Classroom Attendance Systems Based on Bluetooth Low Energy Indoor Positioning Technology for Smart Campus , 2020, Inf..

[15]  Baoguo Yu,et al.  Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning , 2020, Sensors.

[16]  Xianpeng Wang,et al.  A New Array Pseudolites Technology for High Precision Indoor Positioning , 2019, IEEE Access.

[17]  Shuang Li,et al.  An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite , 2019, Sensors.

[18]  Hao Xia,et al.  Indoor Localization on Smartphones Using Built-In Sensors and Map Constraints , 2019, IEEE Transactions on Instrumentation and Measurement.

[19]  Dong Seog Han,et al.  A Combined PDR and Wi-Fi Trilateration Algorithm for Indoor Localization , 2019, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).

[20]  Xiaoji Niu,et al.  Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones , 2018, Sensors.

[21]  Seung-Hyun Lee,et al.  Implementation of Android Application for Indoor Positioning System with Estimote BLE Beacons , 2018 .

[22]  Yongli Ren,et al.  D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate , 2017, Pervasive Mob. Comput..

[23]  Adam C. Winstanley,et al.  Indoor location based services challenges, requirements and usability of current solutions , 2017, Comput. Sci. Rev..

[24]  Paul Meissner,et al.  High-accuracy positioning for indoor applications: RFID, UWB, 5G, and beyond , 2016, 2016 IEEE International Conference on RFID (RFID).

[25]  Hend Suliman Al-Khalifa,et al.  Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances † , 2016, Sensors.

[26]  R. Faragher,et al.  An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications , 2014 .

[27]  Rosdiadee Nordin,et al.  Recent Advances in Wireless Indoor Localization Techniques and System , 2013, J. Comput. Networks Commun..

[28]  Chankil Lee,et al.  Indoor positioning: A review of indoor ultrasonic positioning systems , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).

[29]  G. Tröster,et al.  RoomSense: an indoor positioning system for smartphones using active sound probing , 2013, AH.

[30]  Mark Weber,et al.  Indoor localization with UMTS compared to WLAN , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[31]  Sheikh Tahir Bakhsh,et al.  Indoor positioning in Bluetooth networks using fingerprinting and lateration approach , 2011, 2011 International Conference on Information Science and Applications.

[32]  Carsten Isert,et al.  Self-contained indoor positioning on off-the-shelf mobile devices , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[33]  M. Bocquet,et al.  Using enhanced-TDOA measurement for indoor positioning , 2005, IEEE Microwave and Wireless Components Letters.

[34]  A. Haghighat,et al.  Beep: 3D indoor positioning using audible sound , 2005, Second IEEE Consumer Communications and Networking Conference, 2005. CCNC. 2005.

[35]  Zhiyi Zhou An Overview of Ultra-Wideband Positioning Technology and Its Applications , 2022, SHS Web of Conferences.

[36]  Susanna Kaiser,et al.  A Review of Indoor Localization Methods Based on Inertial Sensors , 2019, Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation.