Project: “Deep Learning for Indoor and Outdoor Positioning”
Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for positioning inside buildings and in urban canyons impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable solution for such scenarios due to its simple concept and good results. Shallow learning algorithms are traditionally used in location fingerprinting. Recently, however, the research community started utilizing deep learning for fingerprinting after witnessing the great success and superiority these methods have over shallow machine learning. Thus, this project aims to develop deep learning-based solutions that can push the boundaries of the filed. These include, for example, utilizing deep learning to enhance positioning accuracy, provide robust performance against measuring noise, and reduce site surveying costs. Additionally, due to the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions, another goal of this project is to collect and make publicly available real-world data, such as WiFi, Bluetooth, Cellular, and sensor measurements, that are annotated with reliable ground truth coordinates.
The codes can be downloaded here.
Recent Related Publications:
- Fahad Alhomayani and Mohammad H. Mahoor, “Improved indoor geomagnetic field fingerprinting for smartwatch localization using deep learning”, International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018.
- Fahad Alhomayani and Mohammad H. Mahoor, “Deep learning methods for fingerprint-based indoor positioning: a review”, Journal of Location Based Services, 2020.
- Fahad Alhomayani and Mohammad H. Mahoor, “Deep learning-based symbolic indoor positioning using the serving eNodeB”, International Conference on Machine Learning and Applications, 2020.
- Fahad Alhomayani and Mohammad H. Mahoor, “OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach”, in press, Scientific Data.
Recent Related Datasets:
- Fahad Alhomayani and Mohammad H. Mahoor, “OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach”, figshare, 2020.
- Fahad Alhomayani and Mohammad H. Mahoor, “Cellular network measurements for symbolic indoor positioning”, figshare, 2020.