bayesian filter
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2022 ◽  
Vol 190 ◽  
pp. 108305
Author(s):  
Xingzi Qiang ◽  
Rui Xue ◽  
Yanbo Zhu
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yushuai Zhang ◽  
Jianxin Guo ◽  
Feng Wang ◽  
Rui Zhu ◽  
Liping Wang

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.


Author(s):  
Feng Yang ◽  
Xinyi Tang ◽  
Tiancheng Li ◽  
Litao Zheng

2021 ◽  
pp. 103156
Author(s):  
Zong-xiang Liu ◽  
Wei Chen ◽  
Qi-yue Chen ◽  
Liang-qun Li

2021 ◽  
Author(s):  
Ruxuan He ◽  
Xiaoxue Feng ◽  
Shuihui Li ◽  
Feng Pan ◽  
Ning Pu

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