Accuracy Indoor Localization Based on Fuzzy Transfer Learning Model

Abstract

Location-based services greatly facilitate people’s daily life, which puts forward higher requirements for the location calculation of target objects in different environments. Since the fingerprint positioning method does not require additional special equipment and easy to implement, it has become one of the most attractive solutions. In order to ensure the positioning accuracy, this method requires complete sampling of the fingerprints of the positioning area, so a lot of sampling costs are required. In particular, when sampling buildings with multiple floors, the labor and time of the entire sampling process will increase dramatically. At the same time, certain floors or rooms may not be allowed to open, so their fingerprints cannot be sampled. In fact, the floor structures of buildings are mostly similar or the same, such as office buildings, hotels. Therefore, this paper proposes a fuzzy transfer learning model and builds the corresponding prototype system FTLoc. On the premise of ensuring the positioning accuracy of different floors, the system greatly reduces the sampling cost of the entire building. First, for the complete fingerprint data (source domain) of a certain floor, we mine the fingerprint features fine-grained to generate a short-term feature set for each sampling point. Then, according to the sparsity and timing of short-term features, we design an optimized SELSTM, and obtain an effective localization model as the PreModel for transfer learning. Finally, fuzzy clustering is used to add category labels to the source domain data and target domain data, and input them into PreModel to realize the localization model transfer, so as to avoid their data distribution differences affecting the transfer effect as much as possible. FTLoc is fully validated in a multi-storey building. According to the experimental results, when using the first floor sampling data as the source domain, the errors of the FTLoc system on the adjacent floor (second floor) are 1.38 meters (sampling rate = 80%) and 2.33 meters (sampling rate = 30%). The average errors in non-adjacent layers (three, four, five) are 1.92 meters (sampling rate = 80%), 2.87 meters (sampling rate = 30%). Compared with traditional migration, the FTLoc system increased by 18.1% and 12.6% respectively. At the same time, the experiment verified that the error jitter multiple of the FTLoc system under different devices and different sampling densities does not exceed 1.5. Therefore, the FTLoc system designed in this paper ensures the transfer learning effect of different floors, and has good robustness and reliability. In actual positioning applications, the system greatly reduces the sampling cost and achieves high-precision positioning at the same time.