With the rapid development of 5G-A technology, Wi-Fi-based indoor fingerprint localization has gained prominence for its high-precision indoor localization. However, conventional methods often suffer from noise interference and indistinct fingerprints in complex environments, reducing the accuracy of localization. To address these issues, the Feature Equalization Localization Method (FELoc) based on Channel State Information (CSI) is proposed in this paper. A unified preprocessing strategy named WaveICA is designed to enhance fingerprint quality by combining Hierarchical Thresholding Wavelet Denoising (HTWD) for noise suppression and Independent Component Analysis (ICA) for extracting independent features, which effectively improves the discriminability of CSI amplitude and phase data from multiple antennas. Additionally, Feature Equalization Localization Network (FELN) is designed for feature extraction and fusion, which employs dual-branch convolution with a Multiscale Channel-spatial Attention Module (MCAM) to enhance salient features and a novel Feature Equalization Module (FEM) to adaptively fuse phase and amplitude representations. Extensive experiments conducted in diverse indoor scenarios demonstrate that FELoc achieves superior localization performance, providing both higher accuracy and stronger robustness compared to other methods.
Long et al. (Sun,) studied this question.