Landmark-based device calibration and region-based modeling for RSS-based localization

During the past decades, many fingerprint-based indoor positioning systems have been proposed and have achieved great success. However, uncontrolled effects of device diversity, signal noise, and dynamic obstacles could recognizably degrade the performance of modern fingerprint-based indoor localization systems. In this paper, to amend the variations in radio signal strengths (RSSs) caused by device diversity, we proposed an automatic device calibration process. Because of device diversity, the sensed RSS would deviate from the trained radio map and thus leads to poor positioning. An RSS transform function could be adopted to calibrate the RSS variation between different devices and overcome the device diversity problem. However, to train the transform function, a data collection process is required. Unlike conventional calibration methods requiring manual data collection, we proposed a landmark-based automatic collection process. Based on the detection of Wi-Fi landmarks, our system could automatically collect pair-wise RSS samples between devices and train the RSS transform function without extra human power. In addition, to well represent the effects of signal noise and dynamic obstacles, a region-based RSS modeling method was also proposed. The proposed modeling method allows our system to perform region-based target localization and utilize more robust region information for localization. Experiments in various environments demonstrate that our system could give a better positioning performance by properly handling the RSS variation caused by signal noise, dynamic environment, and device diversity.