Indoor Positioning based on Bayes Filtering Using Map Information

Hafner, Petra; Moder, Thomas; Wisiol, Karin; Wieser, Manfred

doi:10.1016/j.ifacol.2015.08.133

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…On this basis, the built-in barometer is further used to assist indoor positioning [ 24 , 25 , 26 , 27 ], altitude change monitoring [ 17 ] and motion recognition [ 13 ]. The applications are all based on the pressure-altitude correlation model [ 2 , 24 , 28 , 29 ], expressed as Equation (1):

where p abs ( h ) is pressure, h is altitude, and p 0 and T 0 are the pressure reference and temperature reference, respectively. The reference conditions are usually set as 288.15 K (15 °C), where p 0 = 1013.25 hPa, T 0 = 288.15 K. However, the pressure is not only influenced by the height, but also by the environmental factors, such as weather, temperature, humidity, and wind speed [ 27 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

Liu

Wang

et al. 2018

Sensors

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Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer’s application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

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