WIFI fingerprinting indoor localization system based on spatio-temporal (S-T) metrics

Indoor localization has greatly leveraged applications regarding to location based service (LBS), which witnessed ever-increasing impact on human life. Among the existing localization solutions, WIFI-based received signal strength index (RSSI) fingerprinting is widely used due to desirable features such as universal availability, privacy protection, and low deployment cost. However, to build a robust, accurate RSSI fingerprinting localization system regardless of application occasions confronts two challenges. The first challenge is to construct a fine-grained and up-to-date RSSI map with reasonable labor cost in the training phase, and the second challenge is to deploy effective algorithm in the localization phase. This article illustrates the design and deployment of our indoor localization system targeting at the above mentioned problems. The overall solution is based on five spatio-temporal (S-T) metrics, to improve localization accuracy. Localization performance is evaluated in three indoor scenes at different scales, which show good accuracy with a median error of 1-2m under office environment, and 3-4m accuracy with no less than 70% probability when the environment is extremely crowded and noisy.