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Sensors 2018, 18(11), 4063;

A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
School of Computer Science, Wuhan University, Wuhan 430072, China
College of Aerospace Engineering, Chongqing University, Chongqing, China, 400044
Author to whom correspondence should be addressed.
Received: 10 October 2018 / Revised: 13 November 2018 / Accepted: 19 November 2018 / Published: 21 November 2018
(This article belongs to the Collection Positioning and Navigation)
PDF [2552 KB, uploaded 21 November 2018]


Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used. View Full-Text
Keywords: indoor positioning; fingerprinting positioning; received signal strength indication (RSSI); Bayesian density model indoor positioning; fingerprinting positioning; received signal strength indication (RSSI); Bayesian density model

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Li, Z.; Liu, J.; Yang, F.; Niu, X.; Li, L.; Wang, Z.; Chen, R. A Bayesian Density Model Based Radio Signal Fingerprinting Positioning Method for Enhanced Usability. Sensors 2018, 18, 4063.

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