Threat detection using machine learning and full polarimetric microwave radar

This is a research project to investigate the capabilities of full polarimetric radar for the detection of concealed threats.



The motivation is to investigate the capabilities of full polarimetric microwave radar for the detection concealed threats. Full polarimetric radar can acquire signatures of threat objects when they are concealed behind materials which are opaque materials such as clothing fabric, paper, plastics, fibre glass any many other materials. This opens the possibility for threat items to be detected, recognised or even identified when concealed in fabrics, in boxes, under floors, in walls, above ceilings, in crates, packaging materials. The main questions raised here is just how effective this can be.  



The objectives of the project are to determine if full polarimetric radar has a capability for concealed object detection and if so, develop a commercial route to exploit this. 


Work packages

There are a number of work packaged:

* WP1: Apply decompositions to data 
* WP2: Measure threats on human body and in enclosures at 18-26.5 GHz 
* WP3: Assess opportunities for high/lower frequency operation 
* WP4: Evaluate opportunities for machine learning
* WP4: Design outline prototype full polarimetric radar
* WP5: Develop exploitation route/technology roadmap 
* WP7: Dissemination of project results 

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