Abstract
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.
Highlights
In recent years, the demand for location-based services (LBSs), both indoors and outdoors, has been gaining attention and has massive demand in industry and academia [1]
Considering the outstanding performance of the density peak clustering (DPC) algorithm in feature selection, we propose a novel density peak clustering algorithm based on the comparison distance (CDPC) algorithm to select several center points of magnetic field strength (MFS), combined it with a Wi-Fi signal to improve the robustness of the proposed localization system
CDPC algorithm is first used to find out the center points of MFS, and these selected MFSs are leveraged measurements were converted into fingerprint grayscale image for localization
Summary
Newly this constructed model was usedwas to used further train with fingerprint image database. 331 thisFinally, newly constructed model to further trainthe with the fingerprint image database. This transfer learning-based wasas used thelocalization final localization model called the fine localizer. In this paper, MLP-based transfer learning is leveraged to fine-tune the Resnet and further increase the localization accuracy. We reserved the trained Resnet model and added MLP after it This newly constructed model was used to further train with the fingerprint image database. This transfer learning-based model was used as the final localization model called the fine localizer.
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