Sensor Fusion and Management in Autonomous Airborne Platforms (Leonardo)

Objectives: 

1. Develop new adaptive data-driven sensor tasking and management strategies.
2. Explore combination of Bayesian signal processing and modern machine learning solutions.
3. Implement and evaluate developed techniques in simulated and real world scenarios.

Description:

Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this project will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple airborne autonomous collaborative platforms (ACPs).

The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, this project could consider aspects related to wide area motion imaging (WAMI), position, navigation, and timing issues (PNT); robustness to adversarial attack; sensor fusion and tracking applications; use of machine learning and Monte Carlo methods. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

This project will be jointly supervised by Professor James Hopgood and Professor Mike Davies.

Research Theme 

Sensor Signal Processing

Industry Partner

Leonardo (as part of the Prosperity Partnership Smart Products Made Smarter) 

Applications

First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.

UK 2:1. in computer science / computer systems or MSc in geographical information science.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. 

Further information on English language requirements for EU/Overseas applicants.

Further information and other funding options.