An improved particle filter based indoor tracking system via joint Wi-Fi/PDR localization

The development of indoor localization has been advanced by the rapid development of intelligent devices. The well-known methods used for indoor localization such as Wi-Fi fingerprint database positioning and pedestrian dead reckoning (PDR) can be implemented in a self-contained smartphone. However, the existing Wi-Fi fingerprint database positioning method can be easily influenced by the dynamic environment while PDR will generate a cumulative error with an increase in walking steps. In this paper, we propose a new hybrid method using PDR and Wi-Fi information. We divide the localization area into several subareas to improve the accuracy of the Wi-Fi fingerprint matching phase and introduce an enhanced particle filter (PF) algorithm which includes subarea information in the state vector and adopts a clonal selection algorithm (CSA) to improve resampling. We conduct a series of experiments in real-world environments, and the experimental results validate that the proposed algorithm is much better than ordinary PF algorithms and standalone methods.