Indoor Positioning Using WiFi RSSI Trilateration and INS Sensor Fusion System Simulation

Indoor positioning is needed in many localization applications such as navigation, autonomous robotic movement, and asset tracking. In this paper, an indoor localization method based on fusion of WiFi RSSI positioning and inertial navigation system (INS) is proposed. Using WiFi positioning only is affected by the indoor communications environment that distort the RSSI signals, also using INS standalone solution has very degraded long-term performance, a Kalman filter (KF) is adopted in this paper to fuse and filter the RSSI signals with the INS data to have more accurate positioning results with average distance error of 0.6m.

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

[2]  Károly Farkas,et al.  Investigation of WLAN Access Point Placement for Indoor Positioning , 2012, EUNICE.

[3]  Mohamad Yassin,et al.  Performance comparison of positioning techniques in Wi-Fi networks , 2014, 2014 10th International Conference on Innovations in Information Technology (IIT).

[4]  Sebastian Fudickar,et al.  Most accurate algorithms for RSS-based Wi-Fi indoor localisation , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

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

[6]  Nobuo Kawaguchi,et al.  Indoor positioning method integrating pedestrian Dead Reckoning with magnetic field and WiFi fingerprints , 2015, 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU).

[7]  Xiaoji Niu,et al.  A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors , 2016, IEEE Communications Letters.

[8]  Yanqin Yang,et al.  An Indoor and Outdoor Seamless Positioning System Based on Android Platform , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[9]  Xuewen Liao,et al.  A segment-based fusion algorithm of WiFi fingerprinting and pedestrian dead reckoning , 2016, 2016 IEEE/CIC International Conference on Communications in China (ICCC).

[10]  Moe Z. Win,et al.  A smartphone localization algorithm using RSSI and inertial sensor measurement fusion , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[11]  Yuan Feng,et al.  RSSI-based Algorithm for Indoor Localization , 2013 .

[12]  Chansik Park,et al.  IMU-assisted nearest neighbor selection for real-time WiFi fingerprinting positioning , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[13]  Gerd Scholl,et al.  Accurate Indoor Localization by Combining IEEE 802.11 g/n/ac WiFi-Systems with Strapdown Inertial Measurement Units , 2014 .

[14]  Oliver J. Woodman,et al.  An introduction to inertial navigation , 2007 .

[15]  Xuewen Liao,et al.  A hybrid indoor positioning algorithm based on WiFi fingerprinting and pedestrian dead reckoning , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

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

[17]  M. Bin Ismail,et al.  Implementation of Location Determination in a Wireless Local Area Network (WLAN) Environment , 2008, 2008 10th International Conference on Advanced Communication Technology.

[18]  Hao Jiang,et al.  Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon , 2017, 2017 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL).

[19]  Sunwoo Kim,et al.  PDR/fingerprinting fusion indoor location tracking using RSS recovery and clustering , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[20]  Lu Qian,et al.  A hybrid indoor positioning algorithm based on WiFi fingerprinting and pedestrian dead reckoning , 2016 .