Advanced Target discrimination using fingerprinting based on High Resolution Range and micro-Doppler profiles

Ground-based airborne imaging radar systems are frequently required to provide enhanced situational awareness information beyond target detection and tracking. In particular, target recognition is an important challenging task that modern radars are frequently requested to provide. Understanding the characteristics of targets, including profiles, polarimetric responses and micro-motions is fundamental to be able to characterise and discriminate cooperative and non-cooperative targets such as vehicles, ballistic missiles and ships. Novel radar systems are now able to provide enriched target information, such as High Resolution Range Profiles (HRRPs) and micro-Doppler (MD) signatures, however there has been much less research reported on how to exploit and maximise the benefits of these systems for the target recognition task. This project will develop models and algorithms to exploit and assess the capabilities of what we will call " a target's fingerprint" extracted from both HRRP and MD to perform advanced target recognition.
Micro-motion characteristics of different targets will be utilised in order to extract unique signatures leading to accurate discrimination between a number of classes. Of particular interest is the derivation of realistic motion models of targets and the way that different motion and observation parameters, e.g. manoeuvring and observation angle, affect the received radar returns. Individual characteristics in range and Doppler domain will be combined and used to generate both detailed, image-like representations and more abstract feature vectors through novel signal processing approaches.

Aim: Develop models, signal processing solutions and systems (e.g waveforms) in order to discriminate targets by exploiting a fusion of High Resolution Range and micro-Doppler profiles.

Objectives:
- Understand the principles of High Resolution Range and Micro-Doppler profiles and signature extraction;
- Develop joint models for Range-Doppler representation of targets;
- Develop supervised and unsupervised (Deep Learning) frameworks for target fingerprinting;
- Develop algorithms for target fingerprinting based target tracking in multi-target scenario;
- Develop algorithms for target behaviour prediction based on fingerprinting and tracking (patterns of life);
- Acquire experimental data in controlled environment using radar sensors available at Strathclyde;
- Validate models and algorithms on lab data;
- Validate models and algorithms on data provided by Leonardo;