Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter

Sensors-based and radio frequency (RF)-based indoor localization technology is one of the keys in location-based services. The IEEE 802.11-2016 introduced the Wi-Fi fine timing measurement (FTM) protocol, which provides a new approach for Wi-Fi-based indoor localization. However, Wi-Fi signals are susceptible to complex indoor environments. To improve the positioning accuracy and stability, an enhanced particle filter (PF) with two different state update strategies, a new criterion for divergence monitoring and rapid re-initialization is proposed to integrate the advantages of pedestrian dead reckoning (PDR) and Wi-Fi FTM. In addition, an adaptive tilt compensation is proposed to improve real-time heading estimation of conventional PDR, and the Wi-Fi FTM outliers are detected by displacement estimation of the PDR. The experimental results show that the proposed PF has better localization performance than the single source positioning methods in a typical indoor scenario. The accuracy of final localization is within 1 m in 86.7% of the dynamic cases and the average calculation time is less than 0.5 s when the number of particles is 2000.

[1]  Mohamed Ibrahim,et al.  Verification: Accuracy Evaluation of WiFi Fine Time Measurements on an Open Platform , 2018, MobiCom.

[2]  Lian Zhao,et al.  Synergism of INS and PDR in Self-Contained Pedestrian Tracking With a Miniature Sensor Module , 2010, IEEE Sensors Journal.

[3]  Ruizhi Chen,et al.  A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS , 2012, Sensors.

[4]  Yunho Jung,et al.  IEEE 802.15.4 ZigBee-Based Time-of-Arrival Estimation for Wireless Sensor Networks , 2016, Sensors.

[5]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  I. Bar-Itzhack,et al.  Novel quaternion Kalman filter , 2002, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Yuan Zhang,et al.  Pedestrian dead reckoning for MARG navigation using a smartphone , 2014, EURASIP J. Adv. Signal Process..

[8]  Leor Banin.  WiFi FTM and Map Information Fusion for Accurate Positioning , 2016 .

[9]  Urs Niesen,et al.  Intervehicle Range Estimation From Periodic Broadcasts , 2017, IEEE Transactions on Vehicular Technology.

[10]  Ofer Bar-Shalom,et al.  A Machine Learning Approach for Wi-Fi RTT Ranging , 2019, Proceedings of the 2019 International Technical Meeting of The Institute of Navigation.

[11]  Yuwei Chen,et al.  Inferring Human Activity in Mobile Devices by Computing Multiple Contexts , 2015, Sensors.

[12]  Yuwei Chen,et al.  Sensing strides using EMG signal for pedestrian navigation , 2011 .

[13]  S. Beauregard,et al.  Indoor PDR performance enhancement using minimal map information and particle filters , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[14]  F Gustafsson,et al.  Particle filter theory and practice with positioning applications , 2010, IEEE Aerospace and Electronic Systems Magazine.

[15]  H. Haas,et al.  Pedestrian Dead Reckoning : A Basis for Personal Positioning , 2006 .

[16]  Xiaoping Yun,et al.  Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking , 2006, IEEE Trans. Robotics.

[17]  L. Banin,et al.  Next Generation Indoor Positioning System Based on WiFi Time of Flight , 2013 .

[18]  Kyandoghere Kyamakya,et al.  An Indoor Bluetooth-Based Positioning System: Concept, Implementation and Experimental Evaluation , 2003, International Conference on Wireless Networks.

[19]  M. Newman,et al.  Opportunistic radio SLAM for indoor navigation using smartphone sensors , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[20]  Liang Chen,et al.  A Robust Filter for TOA Based Indoor Localization in Mixed LOS/NLOS Environment , 2018, 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS).

[21]  Chadly Marouane,et al.  Indoor positioning using smartphone camera , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[22]  Yu Liu,et al.  Analysis for Low Cost Inertial Sensors Based Pedestrian Dead Reckoning , 2014 .

[23]  Ruizhi Chen,et al.  A Pose Awareness Solution for Estimating Pedestrian Walking Speed , 2018, Remote. Sens..

[24]  François Marx,et al.  Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning , 2006, EURASIP J. Adv. Signal Process..

[25]  Guoliang Chen,et al.  Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization , 2015, Sensors.

[26]  Hui Fang,et al.  Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience , 2005, IEEE Transactions on Instrumentation and Measurement.

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

[28]  Leor Banin,et al.  Enhanced WiFi ToF indoor positioning system with MEMS-based INS and pedometric information , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[29]  Yuwei Chen,et al.  A Smart Phone Based PDR Solution for Indoor Navigation , 2011 .

[30]  Joseph Menke.  Multi-Modal Indoor Positioning of Mobile Devices , 2015 .

[31]  Richard P. Martin,et al.  Restarting Particle Filters: An Approach to Improve the Performance of Dynamic Indoor Localization , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[32]  Ofer Bar-Shalom,et al.  Scalable Wi-Fi Client Self-Positioning Using Cooperative FTM-Sensors , 2019, IEEE Transactions on Instrumentation and Measurement.

[33]  Alessandro Neri,et al.  Indoor Vehicle Localization Based on Wi-Fi Navigation Beacons for Multi-Modal Transportation Applications , 2019 .

[34]  Henri Nurminen,et al.  An efficient indoor positioning particle filter using a floor-plan based proposal distribution , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[35]  Valerie Renaudin,et al.  Quaternion based heading estimation with handheld MEMS in indoor environments , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[36]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[37]  Bahram Honary,et al.  A Sensor Fusion Method for Smart phone Orientation Estimation , 2012 .

[38]  Ruizhi Chen,et al.  A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors , 2019, Remote. Sens..

[39]  Henri Nurminen,et al.  Particle filter and smoother for indoor localization , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[40]  François Le Gland,et al.  Information fusion for indoor localization , 2009, 2009 12th International Conference on Information Fusion.

[41]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[42]  Huai-Rong Shao,et al.  WiFi-based indoor positioning , 2015, IEEE Communications Magazine.

[43]  Chen Liang,et al.  Indoor Positioning with Smartphones:The State-of-the-art and the Challenges , 2017 .

[44]  Youngnam Han,et al.  SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization , 2015, IEEE Sensors Journal.

[45]  Myo-Taeg Lim,et al.  Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering , 2015, IEEE Transactions on Industrial Informatics.

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