Sensor Signal Processing

Sensor Signal Processing (SSP) develops Intelligence, Surveillance and Reconnaissance (ISR) concepts that can prioritize, process, and fuse large amounts of information from heterogeneous sensors on dynamic and static platforms generating data of different types and varying quality, in an efficient and timely manner.

This theme develops AI-powered technologies to make sense of sensory data, and to enhance automated and Human-In-The-Loop (HITL) semi-automated decision-making.

Exemplar projects include:

  • Target detection and tracking using neuromorphic computing
  • Distributed Sensor Networks in GPS Denied Environments
  • Explainable, semantic image encoding based on vision transformers

This theme interacts substantially with Autonomous Sensing Platforms, ensuring processed data is fit-for-purpose in the computational pipeline.

photograph of scientific equations on a blackboard

Theme leads: 

Davies, Hopgood, Altmann, Repetti);