Paper Info
Title
WiFi Based Fingerprinting Positioning Based on Seq2seq Model.
Abstract
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.
Year
DOI
Venue
2020
10.3390/s20133767
SENSORS
Keywords
DocType
Volume
WiFi based positioning,seq2seq model,deep learning,trajectory
Journal
20
Issue
ISSN
Citations 
13.0
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Haotai Sun100.34
Xiaodong Zhu200.34
Yuan-Ning Liu316022.94
Wen-Tao Liu411.05