Indoor positioning using Wi-Fi fingerprinting pedestrian dead reckoning and aided INS
A.A. Panyov, Andrey A. Golovan, А. В. Смирнов
Abstract
A.A. Panyov, Andrey A. Golovan, А. В. Смирнов
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.
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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.
Key concepts: Dead reckoning, Gyroscope, Inertial navigation system, Accelerometer, Kalman filter, Computer science, Pedestrian, SIGNAL (programming language)