| Summary: | 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 (<i>RSS</i>) fingerprint database yield high labor. Moreover, the sparsity and stability of <i>RSS</i> fingerprinting datasets can be the most dominant error sources. This work proposes a minimally Semi-simulated <i>RSS</i> Fingerprinting (SS-RSS) method to generate and maintain dense fingerprints from real spatially coarse <i>RSS</i> 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 <i>RSS</i> 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.
|
|---|
