WiFi Based Fingerprinting Positioning Based on Seq2seq Model
Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/13/3767 |
| _version_ | 1797563293455351808 |
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| author | Haotai Sun Xiaodong Zhu Yuanning Liu Wentao Liu |
| author_facet | Haotai Sun Xiaodong Zhu Yuanning Liu Wentao Liu |
| author_sort | Haotai Sun |
| collection | DOAJ |
| description | Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods. |
| first_indexed | 2024-03-10T18:40:36Z |
| format | Article |
| id | doaj.art-7298e22a1c154868bb6a500c3458be38 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:40:36Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7298e22a1c154868bb6a500c3458be382023-11-20T05:53:22ZengMDPI AGSensors1424-82202020-07-012013376710.3390/s20133767WiFi Based Fingerprinting Positioning Based on Seq2seq ModelHaotai Sun0Xiaodong Zhu1Yuanning Liu2Wentao Liu3School of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Technology, Jilin University, Changchun 130012, ChinaIndoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.https://www.mdpi.com/1424-8220/20/13/3767WiFi based positioningseq2seq modeldeep learningtrajectory |
| spellingShingle | Haotai Sun Xiaodong Zhu Yuanning Liu Wentao Liu WiFi Based Fingerprinting Positioning Based on Seq2seq Model Sensors WiFi based positioning seq2seq model deep learning trajectory |
| title | WiFi Based Fingerprinting Positioning Based on Seq2seq Model |
| title_full | WiFi Based Fingerprinting Positioning Based on Seq2seq Model |
| title_fullStr | WiFi Based Fingerprinting Positioning Based on Seq2seq Model |
| title_full_unstemmed | WiFi Based Fingerprinting Positioning Based on Seq2seq Model |
| title_short | WiFi Based Fingerprinting Positioning Based on Seq2seq Model |
| title_sort | wifi based fingerprinting positioning based on seq2seq model |
| topic | WiFi based positioning seq2seq model deep learning trajectory |
| url | https://www.mdpi.com/1424-8220/20/13/3767 |
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