Robust Data-Fusion for Drone Surveillance Systems Using Cross-Domain AI

Drone-based Earth observation, surveillance, and goods delivery solutions are among the recently enabled technologies that heavily rely on the reliability of on-board sensors and the integrated processing of collected data. Such sensor systems can be unreliable or occasionally unavailable in certain modalities, such as cameras and LiDAR in foggy, cloudy, or dusty environments, and radar in radio-frequency congested or denied environments.

The aim here is to conduct low-shot training of cross-domain AI models in order to: (1) improve the overall reliability of data analysis when some modalities are absent or noise-contaminated in harsh situations, and (2) enhance the overall accuracy of existing models.

We aim to extend the scientific understanding and basic technology solutions for drone-based sensor platforms operating in urban environments. These solutions have various applications in security, defence, climate change monitoring, and humanitarian domains, such as disaster surveillance, search and rescue, and urban planning surveillance.

Please note that this advert might close sooner once a suitable candidate is found. Therefore early applications are advised. 

Drone surveillance has various potential applications of interest to relevant industry. While such flying objects are equipped with multiple sensors, a reliable data-adaptive sensor fusion framework is required to accommodate erroneous measurements and provide the necessary precision and reliability for the task. This PhD project specifically aimed to address the following key research questions:

“Accuracy gap”: How can data from different modalities, with varying accuracies, be combined to improve overall decision performance, such as detection, tracking, and classification accuracy?

“Reliability gap”: How can the unreliability of certain modalities be identified and mitigated using limited data from other modalities?

“Generalisation gap”: How robust is the solution to distribution shifts in the inputs, i.e., slight changes in the mode of operation such as day–night transitions?

• a 1st Class undergraduate degree (or equivalent);
• University’s English language requirements. 
   

Please note that funding for this project is currently pending confirmation. Once ratified, the funding will be available to home applicants only (UK+EU settled/pre-settled). For more information on this, please contact the project spervisor, Dr Mehrdad Yaghoobi (m.yaghoobi-vaighan@ed.ac.uk). 

Further information and other funding options.