Partitioning of Radar Signals in Stationary and Ground Moving Targets by Use of Low-Rank and Compressed Sensing Methods.

The aim of airborne ground moving target indication (GMTI) is to detect targets moving relative to the earth surface and to estimate their positions, velocities, moving directions, etc. It allows to build up situational awareness of a region, which is not only of interest for military purposes but also for civilian traffic applications, disaster management etc. In order to successfully detect a moving object, reflections from the earth surface, a.k.a. clutter, need to be suppressed.

The objective of this research is to improve the radar detection capabilities in severe heterogeneous clutter scenarios, e.g. land-sea transitions, present strong clutter discretes like buildings, busy environments such as highway junctions etc. This is achieved by leveraging compressed sensing and low-rank techniques. It overcomes the shortcomings of traditional signal processing, namely space-time adaptive processing, by avoiding the need for training data. Furthermore, a low-rank based robust procedure to estimate the platform velocity and aspect angles is presented. The above mentioned techniques ultimately lead to superior detection performance and reduced false alarm rate, contributing to enhanced GMTI capabilities.