An Indoor Localization Method for Pedestrians Base on Combined UWB/PDR/Floor Map

This paper propose a scheme for indoor pedestrian location, based on UWB (Ultra Wideband)/PDR (Pedestrian Dead Reckoning) and Floor Map data. Firstly, a robust algorithm that uses Tukey weight factor and a pathological parameter for UWB positioning is proposed. The ill-conditioned position problem is solved for a scene where UWB anchors are placed on the same elevation of a narrow corridor. Secondly, a heading angle-computed strategy of PDR is put forward. According to the UWB positioning results, the location of pedestrians is mapped to the Floor Map, and 16 possible azimuth directions with 22.5° interval in this position are designed virtually. Compared to the heading angle of PDR, the center direction of the nearest interval is adopted as the heading. However, if the difference between the head angles of PDR and the nearest map direction is less than five degrees, the heading angle of PDR is regarded as the moving heading. Thirdly, an EKF (Extended Kalman Filter) algorithm is suggested for UWB/PDR/Floor Map fusion. By utilizing the positioning results of UWB, PDR, and the possible heading angle of Floor Map, high precision positioning results are acquired. Finally, two experimental scenarios are designed in a narrow corridor and computer room at a university. The accuracy of pedestrian positioning when all the data are available is verified in the first scenario; the positioning accuracy of a situation where part of UWB is unlock is verified in the second scenario. The results show that the proposed scheme can reliably achieve decimeter-level positioning.

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

[2]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[3]  Thomas Jost,et al.  Cooperative simultaneous localization and mapping for pedestrians using low-cost ultra-wideband system and gyroscope , 2018, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[4]  Simone Morosi,et al.  Improved PDR Localization via UWB-Anchor Based on-Line Calibration , 2018, 2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI).

[5]  Ke Wang,et al.  A dual-infrared-transmitter optical wireless based indoor user localization system with high accuracy , 2017, 2017 Optical Fiber Communications Conference and Exhibition (OFC).

[6]  Jin feng Li,et al.  An Autonomous Waist-Mounted Pedestrian Dead Reckoning System by Coupling Low-Cost MEMS Inertial Sensors and GPS Receiver for 3D Urban Navigation , 2014 .

[7]  David Akopian,et al.  Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges , 2016, IEEE Communications Surveys & Tutorials.

[8]  Ye Kuang,et al.  A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians , 2017, Sensors.

[9]  Guobin Shen,et al.  Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing , 2015, IEEE Journal on Selected Areas in Communications.

[10]  Subrat Kar,et al.  Indoor localization using analog output of pyroelectric infrared sensors , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[11]  Guowei Shi,et al.  Survey of Indoor Positioning Systems Based on Ultra-wideband (UWB) Technology , 2016 .

[12]  Fernando Seco Granja,et al.  Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis , 2017, IEEE Transactions on Instrumentation and Measurement.

[13]  Naser El-Sheimy,et al.  PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation , 2015, Micromachines.

[14]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[15]  Fuqiang Liu,et al.  Indoor Location Position Based on Bluetooth Signal Strength , 2015, 2015 2nd International Conference on Information Science and Control Engineering.

[16]  Fernando Torres Medina,et al.  Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[17]  Ugur Yayan,et al.  A Low Cost Ultrasonic Based Positioning System for the Indoor Navigation of Mobile Robots , 2015, J. Intell. Robotic Syst..

[18]  Sauro Longhi,et al.  A Biased Extended Kalman Filter for Indoor Localization of a Mobile Agent Using Low-Cost IMU and UWB Wireless Sensor Network , 2012, SyRoCo.

[19]  Chau Yuen,et al.  Indoor Positioning Using Visible LED Lights , 2015, ACM Comput. Surv..

[20]  Xin Li,et al.  UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments , 2018, Mob. Inf. Syst..

[21]  Xiaohu You,et al.  Indoor Positioning for Multiphotodiode Device Using Visible-Light Communications , 2016, IEEE Photonics Journal.

[22]  A. N. Miyadaira,et al.  Reduction of ultrasonic indoor localization infrastructure based on the use of graph information , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[23]  Peter J. Huber,et al.  John W. Tukey's contributions to robust statistics , 2002 .

[24]  Alessio De Angelis,et al.  A constraint approach for UWB and PDR fusion , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[25]  Jian Wang,et al.  A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System , 2015, Sensors.

[26]  Wilhelm Stork,et al.  Hybrid indoor pedestrian navigation combining an INS and a spatial non-uniform UWB-network , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[27]  Luming Zhang,et al.  Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective , 2017, IEEE Transactions on Multimedia.

[28]  Alessio De Angelis,et al.  Analysis of Nonideal Effects and Performance in Magnetic Positioning Systems , 2016, IEEE Transactions on Instrumentation and Measurement.

[29]  D. Alvarez,et al.  Comparison of Step Length Estimators from Weareable Accelerometer Devices , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  W. H. Engelmann,et al.  The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[31]  Junhai Luo,et al.  A Smartphone Indoor Localization Algorithm Based on WLAN Location Fingerprinting with Feature Extraction and Clustering , 2017, Sensors.

[32]  Kevin I-Kai Wang,et al.  A Low-Cost INS and UWB Fusion Pedestrian Tracking System , 2019, IEEE Sensors Journal.

[33]  Mei Zhang,et al.  Multiple information fusion indoor location algorithm based on WIFI and improved PDR , 2016, 2016 35th Chinese Control Conference (CCC).

[34]  F. Daum.  Nonlinear filters: beyond the Kalman filter , 2005, IEEE Aerospace and Electronic Systems Magazine.

[35]  Youngnam Han,et al.  Improved heading estimation for smartphone-based indoor positioning systems , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[36]  M. Bachtler,et al.  Kalman Filter supported WiFi and PDR based indoor positioning system , 2018 .

[37]  Cipriano Galindo,et al.  Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach , 2009, Robotics Auton. Syst..

[38]  Georgios B. Giannakis,et al.  Ultra-wideband communications: an idea whose time has come , 2004, IEEE Signal Processing Magazine.

[39]  Naser El-Sheimy,et al.  Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons , 2016, Sensors.