Abstract
It may be very difficult to receive the signals from satellite positioning systems due to the existing obstacles in indoor environment. Arising from the popularity of smart phones, Wi-Fi based indoor positioning technology has the advantages with convenient deployment and low hardware cost. In this study, we focused on indoor positioning using Wi-Fi fingerprint data that were collected in shopping malls. Due to the volatility of Wi-Fi signals and the high-dimensional sparseness of fingerprint data, we proposed a feature extraction algorithm, called joint multi-task stacked denoising auto-encoder (JMT-SDAE), aiming at reducing the dimensionality of the original fingerprint data and improving the indoor positioning performance in shopping malls. Furthermore, the features extracted by JMT-SDAE and gradient boosting decision tree (GBDT) were merged to construct a hybrid model, named as JMT-SDAE+GBDT. The experimental results based on 13 location datasets showed that the proposed feature fusion model had better positioning accuracy when compared with other existing positioners, and thus confirmed the effectiveness of our proposed feature extraction algorithm through multi-task learning.
Highlights
The popularity of mobile devices has led to a greater usage of various location-based services (LBS)
In order to verify the effectiveness of the proposed JMT-stacked denoising autoencoder (SDAE) algorithm on extracting features from highdimensional and sparse Wi-Fi fingerprint data, we compared our method (JMT-SDAE) with several other positioners, including Gaussian naïve Bayes (GNB), K-nearest neighbor (KNN), decision tree (DT), deep neural network (DNN), and SDAE+DNN
The GNB, DT, and KNN algorithms are directly implemented by scikitlearn [51], while DNN, SDAE+DNN and JMT-SDAE are implemented using the Tensorflow [52]
Summary
The popularity of mobile devices has led to a greater usage of various location-based services (LBS). The Global Navigation Satellite Systems (GNSS), such as GPS, BeiDou, GLONASS, and Galileo, are very mature outdoor positioning solutions that can locate targets and achieve high positioning accuracy in an open outdoor environment [1], [2]. The signal strength is about 10 to 100 times weaker in an indoor environment [4]. It is almost impossible for a GNSS receiver to acquire signals from any satellites due to further attenuation arising from various factors such as no line-ofsight, people movement, multi-path effect, interference and noise, etc. An autoencoder is a three-layer feedforward neural network composed of an input layer, a hidden layer and an output layer.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
