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Personnel Positioning System Method in Building Based on Inertial Sensor
Author(s) -
Qingjun Wang,
Zhendong Mu
Publication year - 2022
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0114
pISSN - 1939-0122
DOI - 10.1155/2022/5945604
Subject(s) - computer science , dead reckoning , global positioning system , inertial navigation system , computer vision , positioning system , pedestrian , acceleration , inertial measurement unit , inertial frame of reference , positioning technology , orientation (vector space) , hybrid positioning system , real time computing , artificial intelligence , simulation , telecommunications , engineering , physics , geometry , mathematics , structural engineering , classical mechanics , quantum mechanics , transport engineering , node (physics)
At present, the global positioning system has been widely used in outdoor positioning and navigation, but in buildings, thick walls made of cement and bricks have a certain blocking effect on satellite signals, resulting in severely weakened GPS signals, and also extremely it may have an impact on the positioning accuracy, so that the positioning task cannot be completed. Inertial sensor is a kind of sensor mainly detecting and measuring acceleration tilt impact vibration rotation and multi-degree-of-freedom motion, is an important part to solve navigation orientation and motion carrier control. Therefore, we need to study the positioning system and positioning method of the people in the building according to the indoor positioning technology. This article mainly uses inertial sensors as indoor positioning technology for related research. This technology mainly includes linked inertial navigation algorithm and pedestrian dead reckoning algorithm. This paper uses pedestrian dead reckoning algorithm to study the positioning system. The research shows that the PDR algorithm designed this time is more accurate for the positioning of pedestrian movement in the scene of the building. Compared with the previous algorithm research, it has increased by 1%-2% accuracy meets the expected accuracy requirements.

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