An Indoor Fusion Localization Method using Pedestrian Dead Reckoning

doi:10.23940/ijpe.18.10.p10.23432353

Abstract

Abstract:

We study the indoor localization problem based on Pedestrian Dead Reckoning (PDR) by analyzing the causes of localization error during pedestrian walking. To optimize the PDR-based localization method, we firstly propose a step-sense indoor localization framework, namely, Stepsense, which can analyze the total acceleration of the accelerometer sensor and obtain the number of pedestrian steps using the peak detection. In the Stepsense framework, the step length is calculated by the difference between the acceleration peak and the trough. The 9DOF (Degree of Freedom) and 6DOF methods are invoked by double-check strategy to make the result of direction estimation more accurate. Secondly, the adaptive error model is used to correct the state of particles in the particle filter, in which map matching and RSS matching are integrated. Finally, both the Stepsense framework and the proposed fusion localization method are examined in detail through experiments.

Submitted on July 9, 2018; Revised on August 8, 2018; Accepted on September 15, 2018
References: 19

Qian Zhao, Peng Luan, Huiqiang Wang, Hongwu Lv, Guangsheng Feng, and Mao Tang. An Indoor Fusion Localization Method using Pedestrian Dead Reckoning [J]. Int J Performability Eng, 2018, 14(10): 2343-2353.

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