Abstract
Abstract
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.
Funder
National Natural Science Foundation of China
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Cited by
18 articles.
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