Wi-Fi and magnetic field fingerprinting-based localization have gained increased attention owing to their satisfactory accuracy and global availability. The common signal-based fingerprint localization deteriorates due to well-known signal fluctuations. In this paper, we proposed a Wi-Fi and magnetic field-based localization system based on deep learning. Owing to the low discernibility of magnetic field strength (MFS) in large areas, the unsupervised learning density peak clustering algorithm based on the comparison distance (CDPC) algorithm is first used to pick up several center points of MFS as the geotagged features to assist localization. Considering the state-of-the-art application of deep learning in image classification, we design a location fingerprint image using Wi-Fi and magnetic field fingerprints for localization. Localization is casted in a proposed deep residual network (Resnet) that is capable of learning key features from a massive fingerprint image database. To further enhance localization accuracy, by leveraging the prior information of the pre-trained Resnet coarse localizer, an MLP-based transfer learning fine localizer is introduced to fine-tune the coarse localizer. Additionally, we dynamically adjusted the learning rate (LR) and adopted several data enhancement methods to increase the robustness of our localization system. Experimental results show that the proposed system leads to satisfactory localization performance both in indoor and outdoor environments.

Due to the explosive development of location-based services (LBS), localization has attracted significant research attention over the past decade. Among the associated techniques, wireless fingerprint positioning has garnered much interest due to its compatibility with existing hardware. At present, with the widespread deployment of long-term evolution (LTE) networks and the uniqueness of wireless information fingerprints, fingerprint positioning based on LTE networks is the mainstream method for outdoor positioning. However, in order to improve its accuracy, this method needs to collect enough data at a large number of reference points, which is a labor-intensive task. In this paper, experimental data are collected at different reference points and then converted into wavelet feature maps. Then, a Deep Convolutional Generative Adversarial Network (DCGAN) is leveraged to generate a symmetric fingerprint database. Localization is then carried out by the proposed Deep Residual Network (Resnet), which is capable of learning reliable features from a fingerprint image database. To further increase the robustness of the positioning system, a variety of data enhancement methods are used. Finally, we experimentally demonstrate that the generated symmetric fingerprint database and proposed Resnet reduce the manpower required for fingerprint database collection and improve the accuracy of the outdoor positioning system.