Shenzhen Polytechnic (SZPT), Liuxian Avenue 7098, Shenzhen 518055, China.
School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Luoyu Road 1037, Wuhan 430074, China.
Sensors (Basel). 2023 Mar 3;23(5):2782. doi: 10.3390/s23052782.
Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical (PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.
行人航位推算（PDR）是室内行人跟踪和导航服务的关键组成部分。虽然大多数最近的 PDR 解决方案都利用智能手机中的内置惯性传感器进行下一步估计，但由于测量误差和感测漂移，行走方向、步检测和步长估计的准确性无法保证，导致累积跟踪误差较大。在本文中，我们提出了一种雷达辅助 PDR 方案，称为 RadarPDR，它将调频连续波（FMCW）雷达集成到基于惯性传感器的 PDR 中。我们首先建立了分段墙壁距离校准模型来处理由不规则的室内建筑布局引起的雷达测距噪声，并将墙壁距离估计与智能手机惯性传感器测量的加速度和方位信号融合。我们还提出了一种分层（PF）和扩展卡尔曼滤波器用于位置和轨迹调整。实验已在实际的室内场景中进行。结果表明，所提出的 RadarPDR 是高效和稳定的，优于广泛使用的基于惯性传感器的 PDR 方案。
