A Hybrid Indoor Positioning Algorithm for Cellular and Wi-Fi Networks

Modern communication services are developing rapidly, and indoor positioning technologies are diverse. However, the accuracy of single algorithm cannot meet the actual requirements. To address this issue, an hybrid indoor positioning algorithm for cellular and Wi-Fi networks is proposed in this paper. The proposed algorithm consists of two phases, namely the offline phase and the online phase. In the offline phase, a fingerprint database is reconstructed by principal component analysis (PCA) and interpolation methods to reduce the costs of time. Then, in the online phase, the back propagation (BP) neural network positioning algorithm optimized by adaptive genetic algorithm (AGA-BP) is used for positioning. Moreover, the algorithm uses cellular network positioning to divide sub-regions and then uses Wi-Fi networks to further improve accuracy. The experimental results show that the average positioning error of the proposed hybrid algorithm is 1.70 m, which is 56.0% lower than using Wi-Fi network only.