RP-Fusion:Robust RFID Indoor Localization Via Fusion RSSI and Phase Fingerprint

With the rapid development of the Internet of Things (IoT), indoor localization has become a critical component of numerous applications. Among them, non-contact indoor localization techniques based on Radio Frequency Identification (RFID) fingerprints have garnered significant attention. However, due to the complexity of indoor environments, existing methods achieve satisfactory localization performance on training data, but still face challenges in accurately recognizing the locations of individuals who were not part of the training data. To address these challenges, we propose the RP-Fusion. In our work, we construct a two-stream fusion network to extract fused fingerprint features from both Received Signal Strength Indication (RSSI) and phase. These fused fingerprint features can better map to location characteristics, reduce the impact of individual differences. Experimental results demonstrate the effectiveness of our method in achieving robust localization for untrained individuals, with the accuracy of 99.23%. This outperforms the majority of existing RF fingerprint-based indoor localization results.