Navigation Research Center (NRC), Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, China.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2018 Oct 7;18(10):3349. doi: 10.3390/s18103349.
Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone.
电子设备和铁磁材料在城市环境中的任何建筑物中都很容易找到。在室内步行时，智能手机的航向很难获得稳定的磁场环境和纯地磁场值。因此，本文提出了一种独立的惯性航向校正算法，该算法不涉及磁场，而是充分利用智能手机中嵌入式微机电系统（MEMS）惯性测量单元（IMU）设备。针对行人智能手机定位的严格导航要求，本文重点研究的算法由重力辅助（GA）和中间时间模拟零速度更新（MTS-ZUPT）方法组成。借助 GA 方法，可以根据智能手机重力传感器的数据来判断智能手机的不同使用模式。由于手持智能手机没有零速度状态，因此基于零速度更新（ZUPT）算法的思想提出了 MTS-ZUPT 算法。卡尔曼滤波算法用于在两步的中间时刻抑制航向发散。行走实验结果表明，MTS-ZUPT 算法可以在不借助地磁航向的情况下有效抑制航向误差扩散。当 MTS-ZUPT 方法与 GA 方法集成时，智能手机导航系统可以自主判断使用模式并补偿航向误差。在智能手机使用模式实验中，行人定位精度显著提高，行走误差仅为行走距离的 1.4%至 2.0%。
