Cognitive Radar is the next step and the future of modern radars. More specifically, cognitive radars are remote sensing systems that use priori knowledge for surveillance, tracking, and imaging. These radars can distinguish between signal interference, clutter, and the signal with the use of a receiver to transmitter feedback system. One of the biggest challenges in cognitive radars is modeling cluttering and adapting to its surroundings. This thesis looks into land clutter modeling distributions and discrete clutter modeling techniques. These definitions could be the next step to developing accurate cognitive radar that is more effective in distinguishing clutter from the target.
