Abstract
Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.
Highlights
Location-based services deliver excellent research and commercial value and have become a common object of research
Researchers have used a variety of indoor signals for positioning, including wireless local area network (WLAN)
We propose a merged location fingerprint based on Wi-Fi fingerprints and scene image features
Summary
Location-based services deliver excellent research and commercial value and have become a common object of research. With the expansion of urban areas, human activities in indoor environments are becoming increasingly abundant, and the demand for indoor positioning services is increasing. Smartphones that integrate wireless, visual, and accelerometer sensors can facilitate indoor positioning services [1]. Owing to the complex and variable natures of indoor environments, the large-scale application of indoor positioning solutions has yet to be achieved. Researchers have used a variety of indoor signals for positioning, including wireless local area network (WLAN)
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