Indoor Localization Service based on the Data Fusion of Wi-Fi and RFID

With more and more requirements of navigation for complex indoor environments, the indoor location service has become the hotspot in the field of mobile computing. However, with only one single type of wireless signal, it is difficult to achieve ideal accuracy of positioning in the indoor environments full of indoor noises. In order to improve the performance of indoor location service, we propose a novel indoor localization mechanism, which realizes an effective data fusion of Wi-Fi and RFID signals via on-demand deployment of Wi-Fi access points and RFID tags. This mechanism can eliminate the blind areas of location so as to realize the low-cost and high-accurate indoor localization. In order to further improve the location performance, we put forward the Kalman filter algorithm based on singular value judgment (KFASVJ) and KFASVJ-based indoor localization algorithm (KILA). The KILA is adopted to judge the maximum singular value of Wi-Fi signal wave, so as to optimize the wireless signal wave. KILA can reduce the indoor noise interference with Wi-Fi signals, so to realize a high accuracy of positioning and real-time positioning stability in complex indoor environments. The experimental results and the performance analysis show that KILA achieves a better accuracy of positioning than typical Kalman-filter-based localization algorithm (KFLA), about 13% to 28% accuracy of positioning improvement in the indoor environments with the [35dB, 65dB] indoor noise. KILA has the lower time complexity, the higher location speed and the better stability, and it can maintain a good localization performance even in the indoor environment with the indoor noises changing dynamicly.