Indoor Radio Map Construction and Localization With Deep Gaussian Processes

• Published in: IEEE internet of things journal Vol. 7; no. 11; pp. 11238 - 11249
• Main Authors: Wang, Xiangyu, Wang, Xuyu, Mao, Shiwen, Zhang, Jian, Periaswamy, Senthilkumar C. G., Patton, Justin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Bayesian analysis; Deep Gaussian process; deep learning; Electronic devices; Estimation; Gaussian process; Gaussian processes; indoor localization; Internet of Things; Localization; Location based services; Mobile handsets; Parameter identification; Radio; radio map construction; Signal processing; Signal strength; Training; Training data
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2996564

With the increasing demand for location-based service, WiFi-based localization has become one of the most popular methods due to the wide deployment of WiFi and its low cost. To improve this technology, we propose DeepMap, a deep Gaussian process for indoor radio map construction and location estimation. Received signal strength (RSS) samples are used in DeepMap to generate accurate and fine-grained radio maps. A two-layer deep Gaussian process model is designed to determine the relationship between the location and RSS samples, while the model parameters are optimized with an offline Bayesian training method. To identify the location of a mobile device, a Bayesian fusion method is proposed, which leverages RSS samples from multiple access points (APs) to achieve high location estimation accuracy. We conduct comprehensive experiments to verify the performance of DeepMap in two indoor settings. DeepMap's robustness is validated using limited training data.