Deep learning techniques for passive sensing and indoor localisation

An EPSRC Industrial CASE award PhD studentship is available at the University of Bristol in collaboration with Toshiba TRL.

Accurate indoor localisation is a rich source of information for understanding human behaviour. Video depth cameras and wearable technologies can provide high levels of accuracy and represent the stateof-the-art in indoor localisation. Wearable technologies (such as wristbands) tend to have limited battery life and have a high risk of non-compliance; commercial solutions based on UWB (Time of Arrival - ToA) can offer very accurate positioning, but with power consumption, the battery life of the wearable tag is several hours (too short for long term localisation applications).

This project focusses instead on a next generation of localisation, which exploits ambient Radio Frequency (RF) signals through passive radar technology. RF signals such as those arising from Wi-Fi transmissions typically cover entire households. The reflections of these signals from people provide a rich source that can be used to determine localisation information (and also in some cases physical activity information), without requiring a wearable device and with much lower perceived intrusion.

The project will utilise the latest ideas in Artificial Intelligence /Deep Learning for automatic recognition of physical activities augmented by indoor localisation capabilities. The successful PhD student will join a large team working on the recently funded OPERA project.

Further Particulars

Doing research at the University of Bristol The quality of research at the University of Bristol places it within the top five Universities in the UK based on the Research Excellence Framework and Times higher Education rankings 2014-15. The PhD candidate will be a part of a friendly and diverse community, with the Bristol Doctoral College (BDC) as the focal central coordinating facility. Alongside the specialist training the candidate will receive in PhD-specific topics, the BDC offers approximately 200 courses, interactive workshops and seminars as a part of the University’s Personal and Professional Development Programme for PGR students. The BDC organises University-wide events and provides a hub of information, guidance and resources to help researchers to get the most of their time at Bristol.

Candidate Requirements

We are looking for an enthusiastic student with a First class / minimum 2:1 honours degree, or equivalent, in Computer Science, Electrical and Electronic Engineering, Physics or Maths.

Basic skills and knowledge required:
Solid Mathematics (Statistics, Multivariate Calculus, Optimisation, Matrix Algebra)
Programming: Python, Matlab