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.

High-resolution Object Detection in the Sea Clutter Environments (SAAB Systems)

Complex distribution modelling of the sea clutters to improve the target detection performance in terms of ROC, TP, FP figures.

Development of spiking architectures using Bayesian deep learning

This project consists of developing robust and scalable methods to train spiking artificial neural networks

Distributed Information Fusion

This project will develop new machine learning inspired algorithms to extract, communicate and fuse information from networks of sensors, converting sensing into actionable information.

Autonomy Counter-Counter Measures (Leonardo)

Humans with experience are very able to know when something is going wrong with the system they are using and to compensate for it. In autonomous system this intuition needs to be built into the algorithmic design.

Robust non-linear state-space modeling: From filtering to deep learning approaches

The objective is to mix filtering methods with optimisation and deep learning to design advanced efficient tracking methodologies, with strong theoretical guarantees.

Model based deep learning for robust multimodal 3D imaging

The project aims to develop new computational algorithms to optimize the acquisition and processing of event data (e.g., single-photon data) and enable fast imaging in extreme environmental conditions using statistical and learning based approaches.

Machine learning for position location (AMD)

This project will develop novel Artificial Intelligence (AI) techniques for position location, considering both the algorithm’s performance and its ability to be implemented efficiently in hardware.

Image processing techniques for underwater single photon imaging

The aim of the project is to improve and automate the data processing pipeline of underwater imaging systems currently under development at HWU.

Enhancing spin-based quantum sensors with machine learning

We will apply state-of-the-art signal processing techniques (e.g. online sequential Bayesian inference, reinforcement learning) to make spin-based quantum sensors faster, more robust against noise and changing environments, and more user-friendly, with the goal of detecting weak nanoscale magnetic resonance signals from small ensembles of molecules.

EPSRC and MoD CDT in Sensing, Processing, and AI for Defence and Security (SPADS)

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