A Real-Time Pedestrian Dead Reckoning System With FM-Aided Motion Mode Recognition

Accurate indoor pedestrian positioning has attracted extensive research attention along with the rising popularity of indoor location-based service. Among the indoor positioning methods, pedestrian dead reckoning (PDR) stands out for requiring neither expensive infrastructure nor laborious site survey. However, with time going on, the PDR method suffers from error accumulation problem, whereas the major cause of positioning error is usually heading deviation. To tackle the error-drifting problem, a real-time indoor PDR system using inertial sensors and the frequency-modulated radio receiver is designed in this paper. First, a random forest classifier combining frequency-modulated radio signal and inertial sensor signal is devised to achieve real-time identification of straight-line and turning mode of moving. Based on the classification result, the constraint is applied to headings in straight periods and true headings are extracted from accelerometer and magnetometer measurements to reduce heading deviation in straight periods. Field experiments have been conducted at three sites in two office buildings to evaluate the performance of the proposed system. The experimental results indicate error drifting can be effectively constrained by the proposed PDR system.

[1]  Sheng Liu,et al.  Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System , 2015, Sensors.

[2]  Widyawan,et al.  Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system , 2012, 2012 International Conference on System Engineering and Technology (ICSET).

[3]  Fernando Seco Granja,et al.  Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings , 2012, J. Locat. Based Serv..

[4]  Qiang Shen,et al.  A Handheld Inertial Pedestrian Navigation System With Accurate Step Modes and Device Poses Recognition , 2015, IEEE Sensors Journal.

[5]  H. Weinberg Using the ADXL202 in Pedometer and Personal Navigation Applications , 2002 .

[6]  Robert Harle,et al.  A Survey of Indoor Inertial Positioning Systems for Pedestrians , 2013, IEEE Communications Surveys & Tutorials.

[7]  Zoubir Irahhauten,et al.  Analysis of a UWB Indoor Positioning System Based on Received Signal Strength , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[8]  Sukhan Lee,et al.  Indoor human localization with orientation using WiFi fingerprinting , 2014, ICUIMC.

[9]  Xiaoli Meng,et al.  Use of an Inertial/Magnetic Sensor Module for Pedestrian Tracking During Normal Walking , 2015, IEEE Transactions on Instrumentation and Measurement.

[10]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[11]  Peilin Liu,et al.  Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone , 2015, Sensors.

[12]  Ugur Yayan,et al.  An ultrasonic based indoor positioning system , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[13]  M. Matsumoto,et al.  RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering , 2007 .

[14]  Andrei Popleteev,et al.  Indoor positioning using FM radio signals , 2011 .

[15]  Andrew G. Dempster,et al.  Indoor localization using FM radio signals: A fingerprinting approach , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[16]  Andy Hopper,et al.  Broadband ultrasonic location systems for improved indoor positioning , 2006, IEEE Transactions on Mobile Computing.

[17]  Shih-Hau Fang,et al.  A Group-Discrimination-Based Access Point Selection for WLAN Fingerprinting Localization , 2014, IEEE Transactions on Vehicular Technology.

[18]  Holger Claussen,et al.  Locating user equipments and access points using RSSI fingerprints: A Gaussian process approach , 2016, 2016 IEEE International Conference on Communications (ICC).

[19]  Xiaoli Meng,et al.  Quaternion-Based Kalman Filter With Vector Selection for Accurate Orientation Tracking , 2012, IEEE Transactions on Instrumentation and Measurement.

[20]  Christian Wietfeld,et al.  Design of an UWB indoor-positioning system for UAV navigation in GNSS-denied environments , 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[21]  J. Borenstein,et al.  Heuristic Reduction of Gyro Drift for Personnel Tracking Systems , 2009 .

[22]  Sylvie Lamy-Perbal,et al.  An improved shoe-mounted inertial navigation system , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[23]  Isaac Skog,et al.  Foot-Mounted Inertial Navigation and Cooperative Sensor Fusion for Indoor Positioning , 2010 .

[24]  Samer S. Saab,et al.  A Standalone RFID Indoor Positioning System Using Passive Tags , 2011, IEEE Transactions on Industrial Electronics.

[25]  Yiqiang Chen,et al.  Power-efficient access-point selection for indoor location estimation , 2006, IEEE Transactions on Knowledge and Data Engineering.

[26]  Fernando Seco Granja,et al.  Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements , 2012, IEEE Transactions on Instrumentation and Measurement.

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

[28]  Dong-Hwan Hwang,et al.  A Step, Stride and Heading Determination for the Pedestrian Navigation System , 2004 .

[29]  Samih Eisa,et al.  Removing useless APs and fingerprints from WiFi indoor positioning radio maps , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[30]  Naser El-Sheimy,et al.  Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation , 2016, IEEE Sensors Journal.

[31]  B. Krach,et al.  Cascaded estimation architecture for integration of foot-mounted inertial sensors , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

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

[33]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.