Indoor positioning using Wi-Fi fingerprinting pedestrian dead reckoning and aided INS

Author

Panyov, Alexey A. ; Golovan, Andrey A. ; Smirnov, Alexander S.

Author_Institution

Lab. of Navig. & Control, Lomonosov Moscow State Univ., Moscow, Russia

fYear

2014

fDate

25-26 Feb. 2014

Firstpage

1

Lastpage

2

Abstract

In this paper we propose a method of indoor navigation using a MEMS-based strapdown inertial navigation system (INS) aided by Wi-Fi signal strength measurements. This system does not rely on any special hardware, a modern smartphone with built-in MEMS sensors (accelerometers and gyroscopes) is sufficient for navigation. The developed INS navigation algorithm is built on the basis of the Kalman Filter solutions using the INS dead reckoning. It operates with positional data provided by Wi-Fi signal strength measurements and Pedestrian Dead Reckoning (PDR). The experimental results demonstrate the feasibility of operating this system with an accuracy of σ = 1.5 m.

Keywords

Global Positioning System; Kalman filters; accelerometers; gyroscopes; inertial navigation; microsensors; radiotelemetry; smart phones; wireless LAN; Kalman filter; MEMS sensor; MEMS-based strapdown inertial navigation system; PDR; Wi-Fi fingerprinting; Wi-Fi signal strength measurement; accelerometer; aided INS; gyroscope; indoor navigation method; indoor positioning; pedestrian dead reckoning; smartphone; Accuracy; Dead reckoning; Filtering algorithms; Fingerprint recognition; IEEE 802.11 Standards; Particle filters; Kalman filter; Pedestrian Dead Reckoning; Wi-Fi fingerprinting; indoor positioning; strapdown INS;

fLanguage

English

Publisher

ieee

Conference_Titel

Inertial Sensors and Systems (ISISS), 2014 International Symposium on

Conference_Location

Laguna Beach, CA

Type

conf

DOI

10.1109/ISISS.2014.6782540

Filename

6782540