Adaptable Map Matching Using PF-net for Pedestrian Indoor Localization

Abstract

map matching has played a crucial role in technologies related to indoor positioning. Conventional map matching algorithms based on particle filter (PF) have some limitations, such as the limited use of map information, poor generalization and low precision. To solve these problems, we propose an adaptable particle filter network (AdaPFnet), a novel map matching technique that integrates particle filter algorithm into a neural network. AdaPFnet uses local views of particles to represent particles so that the map information about location can be learned sufficiently through a neural network. To demonstrate the performance of the model, it has conducted extensive experiments using 1540 real-world data. The results show that AdaPFnet outperforms PF by up to 21% and remains a strong adaptability for different environments.