Adaptable Map Matching Using PF-net for Pedestrian Indoor Localization
| Autor: | Lijia Zhang, Liang Zhou, Zhuoling Xiao, Mo Cheng, Jun Zhou |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Map matching Net (mathematics) Computer Science Applications Data modeling Euclidean distance Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Trajectory Artificial intelligence Electrical and Electronic Engineering Particle filter Hidden Markov model business |
| Zdroj: | IEEE Communications Letters. 24:1437-1440 |
| ISSN: | 2373-7891 1089-7798 |
| DOI: | 10.1109/lcomm.2020.2984036 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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