Fusing acoustic ranges and inertial sensors using a data and model dual-driven approach

doi:10.11947/j.AGCS.2022.20220169

Abstract

Abstract: BDS started providing services worldwide since 2020. It can offer centimeter level positioning service when an open sky is available. BDS is now making a step further to become a more ubiquitous, integrated and intelligent system. At the meantime, high precise indoor positioning techniques are still under developments. Among these techniques, Apple has adapted the ultra-wideband (UWB) technique to iPhone and tried to push this technique to mass-market. While other new positioning techniques such as 5G, acoustic ranging, WiFi round-trip-time (RTT) and bluetooth (BT) angle of arrive (AoA) which support pervasive smartphones are alse competitive. For indoor positioning, it is still facing the challenges of low accuracy, high cost, small signal coverage and limited capability of generalization. Fusing multiple positioning sources method is one of the important approaches to solve these problems. Especially the fusing combination of low-cost inertial positioning source and high-accuracy radio frequency/acoustic positioning source has practical applicable value at present. Pedestrian dead reckoning (PDR) positioning source based on inertial sensors has advantage of the capability to alleviate error accumulation in double integration. However, it is still facing difficulties because of the complex of smartphone holding poses and the diversity of sensor hardware performance. Furthermore, this step-wise approach also limits the position update rate to less than 2 Hz. In order to develop a low-cost, high-precision and wide-coverage indoor positioning solution, a new approach of fusing acoustic ranges and inertial sensors by using a data and model dual-driven method is proposed in this paper. The data driven PDR solution part is developed based on a neural network, it is a deep learning approach by training a network to learn the velocity vector using the inertial measurements as input. The learned velocity vector is then used to propagate the PDR trajectory, which is further integrated with the high precise acoustic ranging measurements by an extended Kalman filter(EKF) in the model driven part. The proposed solution can offer a positioning accuracy of 0.23 meters at a position update rate of 20 Hz.

Key words: indoor localization, smartphone, acoustic signal, pedestrian dead reckoning, multi-source fusion

CLC Number:

P228

CHEN Ruizhi, QIAN Long, NIU Xiaoguang, XU Shihao, CHEN Liang, QIU Chao. Fusing acoustic ranges and inertial sensors using a data and model dual-driven approach[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1160-1171.

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