A Decentralised Cognitive System for Radar Signal Recognition

This project aims to develop a novel distributed radio frequency processing capability for deployment across space-based platforms. The research will focus on elaborating a machine learning (ML) model that can effectively detect and classify conventional and low-probability-of-intercept (LPI) radar signals. The model will dynamically adapt its structure to the collected signals, producing calibrated confidence estimates and learning from misclassified in-distribution samples. To enhance autonomy and resilience to mission contingencies, the neural network will perform asynchronous, distributed training featuring
interruptible and resumable cycles.


Project Leader:
Graham Priestnall, DEWC Systems