Abstract:To address the problem of poor localization performance caused by the small number of fingerprint points and the lack of
representativeness of fingerprint features in the offline phase of fingerprint-based indoor localization, a CSI-based indoor localiza‐
tion method is proposed using Inverse Distance Weighted (IDW) interpolation and amplitude-phase fusion network. The IDW inter‐
polation algorithm is used to generate a large-capacity fingerprint library. Then, a parallel convolutional neural network (CNN) is
used to process the amplitude and phase to obtain the positional fingerprint features. Finally, a novel integrated architecture fusing
Random Forest (RF) and Multilayer Perceptron (MLP) is used for classification to obtain the estimated positions of the target loca‐
tion samples.