Indoor Wi-Fi Localization Based on CNN Feature Fusion Network

Abstract

With the rise of 5G new smart city construction, the demand for location-based services (LBS) has been increasing rapidly. Indoor positioning technology based on Wi-Fi has attracted extensive attention due to its advantages of low deployment cost and high positioning accuracy. However, traditional neural networks ignore a large amount of available information in the intermediate layer when conduct feature extraction of Wi-Fi signal data, resulting in poor localization performance and robustness. In order to solve this drawback, this paper proposes a novel convolutional neural network (CNN) feature fusion network which considers both spatial features and intermediate layer features. Specifically, it normalizes the raw data by z-score to reduce the impact of data fluctuation. Then the spatial features are extracted using CNN and a flatten layer is added after its pooling layer to extract the intermediate layer features. Finally, all features are merged into the fully connected layer. The experimental results show that our proposed fusion network outperforms existing localization algorithms.