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An indoor positioning method based on wireless signal and image

In this paper, we propose a novel smartphone-based indoor localization algorithm by deeply combining wireless signals and images, which leads to improving the localization performance. Although the measured signals are polluted by the noise, we demonstrate that the combination of the image and wireless data dramatically improves the indoor positioning accuracy. Different with common wireless-based indoor positioning methods, the wireless signals that received in a certain time are transformed into frequency domain, and an image named W-image is created by using our proposed approach. Then, SIFT features that are extracted from the W-image is deeply combined with the LBP features that are extracted from the smartphone camera-based images. Moreover, the hybrid features from the smartphone camera images and images of reference database are matched which are used to determine correspondences indoor positioning points. In order to reduce the computation complexity of our proposed method, the wireless signals are illustrated to estimate the coarse positioning points for reducing the search space of image matching. By leveraging wireless signals and images, we are able to achieve almost a 0.86 in mean average precision and 57.65ms in mean average running time. The proposed algorithm can be applied to the smartphones that are integrated cameras to offer high-accuracy location-based services.