3D Indoor Positioning System Based on MEMS Sensors

Abstract:

Nowadays, most of scholar study the 2D indoor positioning system based inertial sensors. But in some case, the information of 3D space about person is very important, such as mine accident, fire rescue, and so on. In this paper, we focus on designing and implementing a 3D indoor positioning system based on MEMS sensors and barometer sensor. The MEMS sensors including a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer is used for acquiring information of human movement. Barometer sensor is used to measure the height of the person where he is. Then the collected raw data is sent to a PC by a MCU wirelessly. Thus the performance of the 3D indoor positioning system is optimized by moving data post processing to a PC. In a 3D indoor positioning system, the integral drift and the heading drift are the main sources of the errors. Therefore, a method which is the combination of the zero velocity updates and the sensor data fusion based on an extended Kalman filter is proposed. Further improvement on the accuracy of positioning can be achieved by removing the static biases. A novel method including compensating the sensor biases based on experiment is presented. These methods have been carefully investigated through theoretical study and simulation. The simulation results indicate 3D indoor positioning is achievable through the application of the presented methods.

• Periodical: Key Engineering Materials (Volumes 645-646)
• Pages: 498-503
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.645-646.498
• Online since: May 2015
• Authors: Jin Lin Zhu, Bing Mo*, Chao Dong Ling, Wen Yuan Fu
• Keywords: 3D Indoor Positioning System, Extended Kalman Filter, Heading Drift, Integral Drift, Sensor Data Fusion

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