Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation
Kalman-Filter-Based Integration of IMU and UWB for High-Accuracy Indoor Positioning and Navigation
复制标题
基于卡尔曼滤波器的 IMU 和 UWB 集成，用于高精度室内定位和导航
DOI：
10.1109/jiot.2020.2965115
复制
发表时间：
2020-04-01
影响因子：
10.6
通讯作者：
Xia, Xiang-Gen
中科院分区：
文献类型：
--
作者：
Feng, Daquan;Wang, Chunqi;Xia, Xiang-Gen
随着智能制造、智能家居等物联网（IoT）应用的兴起，对低成本、高精度的定位导航解决方案需求巨大。惯性测量单元（IMU）可以在短时间内提供精确的惯性导航解决方案，但由于加速度计测量的累积误差，其定位误差随着时间的推移而迅速增大。另一方面，超宽带（UWB）定位导航精度会受到实际环境的影响，即使在视距（LOS）条件下也可能导致不确定跳变。因此，在室内环境中，使用独立的定位导航系统很难达到较高的精度。本文通过扩展卡尔曼滤波（EKF）和无气味卡尔曼滤波（UKF），提出了一种结合IMU和UWB的综合室内定位系统（IPS），以提高鲁棒性和精度。我们还讨论了基站几何分布与精度稀释（DOP）之间的关系，以便合理部署基站。仿真结果表明，IMU提供的先验信息能显著抑制超宽带的观测误差。结果表明，IPS的综合定位导航精度显著提高了仅依赖超宽带测量值的最小二乘（LSs）算法的定位导航精度。该算法具有较高的计算效率，可在一般嵌入式设备上实现实时计算。此外，提出了两种随机运动近似模型算法，并在实际环境中进行了验证。实验结果表明，两种算法在实际IPS中都能达到一定的鲁棒性和连续跟踪能力。
The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.