Abstract
Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, in the offline phase of fingerprinting positioning, the construction and maintenance of a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, the sparsity and stability of RSS fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated RSS Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse RSS acquisitions. This work simulates dense fingerprints exploring the cosine similarity of the directions to Wi-Fi access points (APs), rather than only using either a log-distance path-loss model, a quadratic polynomial fitting, or a spatial interpolation method. Real-world experiment results indicate that the semi-simulated method performs better than the coarse fingerprints and close to the real dense fingerprints. Particularly, the model of RSS measurements, the ratio of the simulated fingerprints to all fingerprints, and a two dimensions (2D) spatial distribution have been analyzed. The average positioning accuracy achieves up to 1.01 m with 66.6% of the semi-simulation ratio. The SS-RSS method offers a solution for coarse fingerprint-based positioning to perform a fine resolution without a time-consuming site-survey.
Highlights
With the rapid development of indoor location-based service (LBS), several indoor positioning technologies have been proposed by researchers, i.e., wireless signal-based localization methods, ultrasonic positioning methods, and computer vision-based methods [1], etc
The training fingerprint dataset is established by scanning Wi-Fi signals from surrounding access points (APs) at site-surveying positions with corresponding labels [8]
With different nearest-neighbor rules, one can note that the SS-Received Signal Strength (RSS) (V4) method outperforms the coarse fingerprints and purely simulated fingerprints and is comparable to the real dense fingerprints
Summary
With the rapid development of indoor location-based service (LBS), several indoor positioning technologies have been proposed by researchers, i.e., wireless signal-based localization methods, ultrasonic positioning methods, and computer vision-based methods [1], etc. The training fingerprint dataset is established by scanning Wi-Fi signals from surrounding APs at site-surveying positions with corresponding labels (e.g., a grid point assigned with a unique label in this work) [8]. A signal path-loss model or quadratic polynomial fitting method [23] is applied to simulate the RSS value of the fingerprint at each non-site-surveying grid [24]. Given the positions of APs, the cosine similarity is explored to select fingerprints for RSS estimation It calculates the direction similarity between the coarse site-surveying grids and the simulated fingerprinting grids. The experiments, implemented in our small-scale indoor scenario, demonstrate that the quadratic polynomial fitting method performs better than the path-loss model, and the positioning accuracy increases with the number of the coarse site-surveying grids. The point with thethe ground truth position and used to to verify the positioning performance
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