A machine learning approach to cognitive radar detection

We consider the requirements of cognitive radar detection in the presence of non-Gaussian clutter. A pair of machine learning approaches based on non-linear transformations of order statistics are examined with the goal of adaptively determining the optimal detection threshold within the low sample support regime. The impact of these algorithms on false alarm rate is also considered. It is demonstrated that the adaptive threshold estimate is effective even when the distribution in question is unknown to the machine learning algorithm.