Optimization of pedestrian navigation algorithm based on reference points of narrow corridor

Author(s):  
Hucheng Wang ◽  
Xiaonan Luo ◽  
Yifan Huang ◽  
Zhi Wang ◽  
Ji Li
Micromachines ◽  
2016 ◽  
Vol 7 (5) ◽  
pp. 91 ◽  
Author(s):  
Tianyu Lin ◽  
Zhenyuan Zhang ◽  
Zengshan Tian ◽  
Mu Zhou

2016 ◽  
Author(s):  
Xiang-bin Meng ◽  
Xian-fei Pan ◽  
Chang-hao Chen ◽  
Xiao-ping Hu

2020 ◽  
Vol 67 (5) ◽  
pp. 3980-3989 ◽  
Author(s):  
Zhihong Deng ◽  
Pengyu Wang ◽  
Tong Liu ◽  
Yun Cao ◽  
Bo Wang

Sensors ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 139 ◽  
Author(s):  
Mingrong Ren ◽  
Kai Pan ◽  
Yanhong Liu ◽  
Hongyu Guo ◽  
Xiaodong Zhang ◽  
...  

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142093093
Author(s):  
Chen Yu ◽  
Luo Haiyong ◽  
Zhao Fang ◽  
Wang Qu ◽  
Shao Wenhua

Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the optimal smoothing algorithm. Secondly, it adjusts the noise parameters adaptively to reestimate current attitudes. Different from the pedestrian dead reckoning-based solution, the error model system in this article contains more state information, which means it is more sensitive and scalable. Moreover, several experiments were conducted, and the experimental results demonstrate that the proposed heading estimation algorithm obtains better performance than previous approaches and our system outperforms the PDR system in terms of flexibility and accuracy.


Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3228 ◽  
Author(s):  
Zun Niu ◽  
Ping Nie ◽  
Lin Tao ◽  
Junren Sun ◽  
Bocheng Zhu

Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can't provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it.


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