Abstract
Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.
Highlights
To date, various indoor positioning technologies have been proposed to meet the increasing requirement of indoor ubiquitous location services [1], such as Pedestrian Dead Reckoning (PDR) [2,3,4], acoustic-based [5,6], visual-based [7,8], radio frequency-based [9,10], and magnetic field-based techniques [11,12]
The constructed fingerprint database includes the coordinates of the reference points, which were measured by an electronic total station and Received Signal Strength Indication (RSSI) from surrounding Access Points (APs)
We proposed a robust and effective Wi-Fi positioning method
Summary
Various indoor positioning technologies have been proposed to meet the increasing requirement of indoor ubiquitous location services [1], such as Pedestrian Dead Reckoning (PDR) [2,3,4], acoustic-based [5,6], visual-based [7,8], radio frequency-based [9,10], and magnetic field-based techniques [11,12]. Wi-Fi positioning techniques can be coarsely divided into two categories: fingerprinting-based [14,15,16,17,18,19] and ranging-based [20,21,22,23,24,25,26,27,28,29,30,31,32,33] The former can be implemented with machine learning methods [10,34,35,36,37] and machine learning-free methods [10,38]. The second is the signal attenuation, which happens frequently in the indoor complex environment This would lead to inaccurate ranging based on RSSI
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