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.
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 callMore From: International Journal of Ad Hoc and Ubiquitous Computing
Disclaimer: 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.
