RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

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.

An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System

“…Meanwhile, a generic framework of RSS has been introduced to enrich the positioning performance [24]. Alternatively, the combination of spatiotemporal constraints in RSS fingerprint-based localization has achieved the same purpose [25]. Compressive sensing has been applied to improve the localization performance in noisy scenarios [26].…”

Modified WiFi-RSS Fingerprint Technique to locate Indoors-Smartphones: FENG building at Koya University as a case study

“…To overcome this problem sparse representation model, namely the Spatio-Temporal Sparse Representation model (ST-SR) is used in [8]. This model integrates the Spatio-Temporal correlation in RSS fingerprint maps and the RSS measurements in the localization procedure.…”