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Lightweight and robust indoor positioning

The aim of this project is to develop a positioning system that works reliably across users, motion modes and device types, is flexible to make the most of available resources (sensor data and maps), and is capable of learning and improving over time. The envisaged system will be able to work flexibly with or without bespoke positioning infrastructure. It should be lightweight enough to run on the user's mobile device without having to offload the position computation to the cloud. The basis of the positioning system is a robust pedestrian dead reckoning algorithm that works accurately for different motion modes (texting, hand swinging, device in trouser / shirt pocket, and so on) despite the noisy nature of inertial measurement units embedded in smart devices. In the presence of environment maps (e.g. floorplans, radio or magnetic fingerprint maps), the positioning system should be able to exploit them to correct the drift of the inertial trajectory, while improving the quality of the available maps as it acquires more data. Finally, the positioning system should be able to learn and improve its accuracy the more it is employed by a user in a particular setting.

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