A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

Abstract

The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.