A Semi-Simulated RSS Fingerprint Construction for Indoor Wi-Fi Positioning
作者： Yuan Yang , Peng Dai , Haoqian Huang , Manyi Wang , Yujin Kuang
DOI: 10.3390/ELECTRONICS9101568
关键词: Stability (probability) 、 Computer science 、 Cosine similarity 、 Pattern recognition 、 Fingerprint database 、 Fingerprint 、 Fingerprint (computing) 、 Artificial intelligence 、 RSS
摘要: Fingerprinting-based Wi-Fi positioning has increased in popularity due to its existing infrastructure and wide coverage. However, the offline phase of fingerprinting positioning, construction maintenance a Received Signal Strength (RSS) fingerprint database yield high labor. Moreover, sparsity stability RSS datasets can be most dominant error sources. This work proposes minimally Semi-simulated Fingerprinting (SS-RSS) method generate maintain dense fingerprints from real spatially coarse acquisitions. simulates exploring cosine similarity directions access points (APs), rather than only using either log-distance path-loss model, quadratic polynomial fitting, or spatial interpolation method. Real-world experiment results indicate that semi-simulated performs better close fingerprints. Particularly, model measurements, ratio simulated all fingerprints, two dimensions (2D) distribution have been analyzed. The average accuracy achieves up 1.01 m with 66.6% semi-simulation ratio. SS-RSS offers solution for fingerprint-based perform fine resolution without time-consuming site-survey.
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