基于数据与模型双驱动的音频/惯性传感器耦合定位方法

北斗卫星导航系统已于2020年实现全球覆盖。在开阔的室外环境,北斗可提供厘米级的定位服务,正向着更泛在、更融合、更智能的综合时空体系迈进。目前高精度室内定位技术处于百花齐放、百家争鸣的状态,尽管苹果支持的超宽带技术在市场中拥有优势,但是5G、音频和蓝牙测角等可支持所有大众手机的技术在市场中也具备竞争力。室内定位目前主要面临部署成本高、定位精度低、信号覆盖范围小和系统泛化能力差等难题。多源融合定位技术是解决这些难题的重要途径之一,特别地,融合低成本惯导定位源和高精度射频/音频定位源是目前具备实用价值的融合定位组合。行人航迹推算(pedestrian dead reckoning,PDR)定位源具有抑制积分误差累积的优势,但是由于用户手机握持姿态的复杂性和手机惯性传感器硬件的差异性,其在相对定位精度、手机泛化能力和多握持姿态支撑等方面也存在劣势,此外,受步频的影响,PDR定位源的位置更新率低于2 Hz。为了实现低成本、高精度和广覆盖的室内定位解决方案,本文提出了一种数据与模型双驱动的多源融合定位新范式,其中数据驱动的PDR部分通过构建神经网络模型,训练加速度传感器和陀螺仪测量值特征,学习速度变化矢量,推算高精度行人航迹,模型驱动部分为将数据驱动输出的相对航迹与高精度定位源输出的观测量通过扩展卡尔曼滤波,实现融合定位输出。试验结果表明,基于数据驱动的PDR方法可提供20 Hz的位置更新率,与高精度音频定位源融合,可实现0.23 m的动态定位精度。

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

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.