This PhD studentship has been developed by the University of Liverpool and STFC’s Hartree centre in partnership with Leonardo, a defence multinational company.
This project is concerned with developing efficient and adaptive object tracking methods based on the Track Before Detect (TBD) technique, adapted to allow prior knowledge to be incorporated into the processing chain and making use of modern Bayesian sampling techniques.
TBD is an established track processing method, which uses information from sub-threshold ‘weak’ detections to improve tracking performance for low contrast objects. The proposed project would look at two main aspect of the problem: the use of lower power/lower cost processors, and the inclusion of modern Bayesian sampling methods to allow the use of supplementary information sources (such as prior information, based on known terrain types and or known object characteristics). The aim would be to develop a scalable processing architecture that allows large numbers of objects to be tracked across a distributed set of processors.
The key challenges are in developing real time processing methods for distributed processors that can use low-power processor systems and using adaptive scheduling to maintain energy efficiency across a number of processors. The non-academic partner (Leonardo) will be involved in defining the problem set and will help by supplying representative data for algorithm development and testing.