Abstract
The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods.
Highlights
In recent years, with the growing applications of Location-Based Service (LBS), wireless localization technology, especially the indoor wireless localization technology becomes an important research topic in wireless network communications
Current indoor localization methods can be roughly divided into three types: (1) localization methods based on special equipment [1,2], which measure the location by using special equipment, such as active bats; (2) the wireless signal ranging methods [3], which measure the location by range measurements such as the Time Of Arrival (TOA) localization method; (3) the methods based on Signal Strength Fingerprint Maps (SSFM) [4,5,6], which first collect the wireless signal strengths of the scene and construct the scene fingerprint maps and match the observed signal intensity of the mobile terminal with the fingerprint maps to obtain the location
We propose an Received Signal Strength (RSS) fingerprint-based indoor localization method by using a revised sparse representation model, namely the Spatio-Temporal Sparse Representation model (ST-SR), which integrates the spatio-temporal correlation in RSS fingerprint maps and the RSS measurements in the localization procedure
Summary
With the growing applications of Location-Based Service (LBS), wireless localization technology, especially the indoor wireless localization technology becomes an important research topic in wireless network communications. RSS strengths in the fingerprint maps and estimate the position of the measurement with high precision To overcome this problem, many researchers have proposed various localization algorithms, such as the KNN method [7,8,9], the Sparse Representation (SR)-based method [10], the Compressed. Sensing (CS)-based method [11,12,13,14,15], etc These fingerprint-based localization methods obtain acceptable positioning performance, most of the current localization methods do not explore and utilize the spatial correlation properties among fingerprint maps, as well as the temporal continuity of the measurements when the user is moving in his/her path.
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.
