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.

Indoor public places are facing more and more security risks, and need to be monitored to find potential anomalies. Benefiting from the advantages of low cost and high privacy, RFID is widely used in indoor monitoring. At present, it has become a common solution to construct the RFID raw data into time sequence trajectory, and then perform preprocessing and cluster analysis. However, there are redundant and uncertain factors in the RFID raw data, which affect the efficiency of anomaly detection. In this paper, we propose BPCluster, a probabilistic-based RFID trajectory anomaly detection algorithm for indoor RFID trajectories. The algorithm incorporates a probabilistic trajectory model, which reduces the redundancy and uncertainty through the context information of trajectories, and then clusters trajectories by the improved LCS algorithm to find abnormal trajectories. Experiments show that BPCluster has better performance in effectiveness and environmental adaptability, and the average accuracy in various environments reaches 91%.