Abstract
Recent years have witnessed a growing interest in using WLAN fingerprint‐based methods for the indoor localization system because of their cost‐effectiveness and availability compared to other localization systems. In this system, the received signal strength (RSS) values are measured as the fingerprint from the access points (AP) at each reference point (RP) in the offline phase. However, signal strength variations across diverse devices become a major problem in this system, especially in the crowdsourcing‐based localization system. In this paper, the device diversity problem and the adverse effects caused by this problem are analyzed firstly. Then, the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression (LR) algorithm. Based on the analysis, the LR algorithm is proposed to create a unique radio map in the offline phase and precisely estimate the user’s location in the online phase. After applying the LR algorithm in the crowdsourcing systems, the device diversity problem is solved effectively. Finally, we verify the LR algorithm using the theoretical study of the probability of error detection. Experimental results in a typical office building show that the proposed method results in a higher reliability and localization accuracy.
Highlights
In recent years, people’s daily life is becoming more and more convenient owing to the development of “5G”, smart cities, and Internet of Things [1, 2], and the network connectivity and spectrum efficiency of Internet of Things are greatly improved by “5G” [3]
Where xi is the i-th fingerprint in the radio map collected by the training device, y is the received signal strength (RSS) values measured in the online phase by localization device, and ðai, biÞ are the coefficients in the mapping function
The localization area is the corridor with 49.4 m in length and 14.1 m in width, which is illustrated with yellow color
Summary
People’s daily life is becoming more and more convenient owing to the development of “5G”, smart cities, and Internet of Things [1, 2], and the network connectivity and spectrum efficiency of Internet of Things are greatly improved by “5G” [3]. In the offline phase, aiming to reduce the labor and time costs of radio map construction, the crowdsourcing method has been proposed in the indoor localization domain, which brings a variety of distinct mobile devices [12, 14, 15]. The relationship of RSS data collected by different devices is linear (2) The fast least trimmed squares (FAST-LTS) algorithm is proposed to eliminate the device diversity problem. Simulation results verify the effectiveness of the proposed algorithm, and all the RSS data are mapped into the same signal space (3) We derived the probability of error detection of all fingerprints in the radio map.
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