An adaptive Wi-Fi indoor localisation scheme using deep learning Online publication date: Tue, 09-Apr-2019
by Chih-Shun Hsu; Yuh-Shyan Chen; Tong-Ying Juang; Yi-Ting Wu
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 30, No. 4, 2019
Abstract: Indoor localisation is an important issue for many indoor applications. Many deep learning-based indoor localisation schemes have been proposed. However, these existing schemes cannot adjust according to different environment. To improve the existing schemes, a novel indoor localisation scheme, which can adaptively adopt the proper fingerprint database according to the collected signals, is proposed in this paper. The proposed Wi-Fi indoor localisation scheme uses two fine-tuning algorithms, namely the cross entropy and the mean squared algorithms, to build the corresponding fingerprint databases. When the standard deviation of the collected signals does not exceed the threshold, the fingerprint database built by the cross entropy algorithm is adopted; when the standard deviation of the collected signals exceed the threshold, the fingerprint database built by the mean squared algorithm is adopted. The experimental results show that the proposed scheme can improve the accuracy of the training data and reduce the localisation error.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook