Crowdsourcing allows for rapid deployment of indoor localization systems. However, compared to the conventional methods, crowdsourcing might collect fewer received signal strength (RSS) values, hence result in greater influence to outliers in RSS values. In this paper, we propose an algorithm to detect such outliers and to substitute them with more suitable RSS values. In particular, we investigate the relationship of RSS values between adjacent locations using a signal propagation model and show that the outliers can be corrected using a signal propagation model. We propose the Signal Propagation-based Outlier Reduction Technic (SPORT) for identifying and adjusting outlier values in both the offline training phase and the online localization phase. Experimental results show that SPORT greatly smoothens the radio map and improves the location accuracy.
['Liye Zhang', 'Shahrokh Valaee', 'Le Zhang', 'Yubin Xu', 'Lin Ma']
Signal propagation-based outlier reduction technique (SPORT) for crowdsourcing in indoor localization using fingerprints