Indoor localization with channel state information images from selected multiple access points

Abstract

To improve the accuracy of indoor localization methods with channel state information (CSI) images, a localization method that used CSI images from selected multiple access points (APs) was proposed. The method had an off-line phase and an on-line phase. In the off-line phase, three APs were selected from the four APs in the localization area based on the received signal strength indication (RSSI). Next, CSI data was collected from the three selected APs using a commercial Intel 5 300 network interface card. A single-channel sub-image was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas. These sub-images were then merged to form a three-channel RGB image, which was subsequently fed into the convolutional neural network (CNN) for training. The CNN model was saved upon completion of training. In the on-line phase, the CSI data from the target device was collected, converted into images using the same process as in the off-line phase, and fed into the well-trained CNN model. Finally, the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities. The proposed method was validated in indoor environments using two datasets, achieving good localization accuracy.

Keywords

WiFi indoor localization / multiple access points / channel state information image / convolutional neural network (CNN) / fingerprint localization / weighted centroid algorithm

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