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.

现代通信业务发展迅速，室内定位技术多样。但是，单一算法的精度不能满足实际要求。为了解决这个问题，本文提出了一种蜂窝和 Wi-Fi 网络的混合室内定位算法。所提出的算法由两个阶段组成，即离线阶段和在线阶段。在离线阶段，通过主成分分析（PCA）和插值方法重建指纹数据库，以减少时间成本。然后在线阶段采用自适应遗传算法(AGA-BP)优化的反向传播(BP)神经网络定位算法进行定位。此外，该算法使用蜂窝网络定位来划分子区域，然后使用 Wi-Fi 网络进一步提高准确性。