Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning
In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware...
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doaj-14c53e8935184907aa78b610f4fbf9fa2020-11-25T03:01:38ZengMDPI AGSensors1424-82202020-04-01202263226310.3390/s20082263Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor PositioningLu Huang0Hongsheng Li1Baoguo Yu2Xingli Gan3Boyuan Wang4Yaning Li5Ruihui Zhu6College of Instrumental Science and Engineering, Southeast University, Nanjing 210018, ChinaCollege of Instrumental Science and Engineering, Southeast University, Nanjing 210018, ChinaState Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, ChinaState Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, ChinaState Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, ChinaState Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, ChinaState Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, ChinaIn view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.https://www.mdpi.com/1424-8220/20/8/2263indoor localizationsmartphonedead reckoningsensorsdeep neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lu Huang Hongsheng Li Baoguo Yu Xingli Gan Boyuan Wang Yaning Li Ruihui Zhu |
spellingShingle |
Lu Huang Hongsheng Li Baoguo Yu Xingli Gan Boyuan Wang Yaning Li Ruihui Zhu Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning Sensors indoor localization smartphone dead reckoning sensors deep neural network |
author_facet |
Lu Huang Hongsheng Li Baoguo Yu Xingli Gan Boyuan Wang Yaning Li Ruihui Zhu |
author_sort |
Lu Huang |
title |
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning |
title_short |
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning |
title_full |
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning |
title_fullStr |
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning |
title_full_unstemmed |
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning |
title_sort |
combination of smartphone mems sensors and environmental prior information for pedestrian indoor positioning |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-04-01 |
description |
In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning. |
topic |
indoor localization smartphone dead reckoning sensors deep neural network |
url |
https://www.mdpi.com/1424-8220/20/8/2263 |
work_keys_str_mv |
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