Advanced Electronic Surveillance DSP and ML Technique

The electromagnetic environment (EME) is becoming increasingly congested and contested. Designers of both radar and communications systems are developing methods that are both more complicated and increasingly harder to detect. Electronic Surveillance (ES) is therefore facing an increasingly complex environment to operate within. As radar and communications systems develop in their adaptability the requirements on ES systems increase significantly.

Recently there have been attempts to bring the advances made in machine learning (ML) to the understanding of the EME. Several works have applied ML to modulation recognition in communications whilst others have attempted to classify individual radar transmitters from their radio frequency emissions (RF). ES could leverage these techniques to provide the operator with greater understanding of the complicated EME around them. Little academic research has been completed on real world applied problems considering low SNR, congested and contested environments.

Many open research questions exist in the application of ML to the Electromagnetic Environment (EME). These include:
- How to collect the training data with limited a priori information or ability to label it.
o Labelling is a significant challenge in general and a non-trivial task to undertake. This PhD develop methods for best practice in this area.
- How to pre-process RF data to get best performance from ML algorithms, e.g., best normalisation techniques to cope with varying power levels and the use of time-frequency transforms to provide more information.
- How to deal with specific ES challenges such as
o Fleeting signals, e.g., only a few examples of a signal/class are available to ML techniques.
o Erroneous data labelling and incomplete datasets
o Congested RF scenarios
o Interference and multi-path problems specific to ES problems.
- How to aggregate ML techniques across multiple distributed sensors
- Benchmarking of ML techniques against traditional methods and defining when either methods break.
- How to deal with multiple signal types - one model or multiple models. Cutting edge solutions may implement hierarchical modelling solutions which can be investigated within this research programme.
- What real world limitations exist when porting algorithms onto hardware

These questions are present across many of the problems within ES that ML could address. The PhD will therefore focus on a particular research area; detecting, counting and separating signals in a congested EME using ML. Through this the PhD student will be able to leverage pervious time-frequency transforms developed at UCL for pre-processing along with current UCL RFSoC work to create datasets for testing and to deploy algorithms on.