This talk deals with problems of covariance matrix estimation in radar/signal processing. Under the widely used Gaussian assumption, the Sample Covariance Matrix (SCM) estimate provides optimal results in terms of estimation performance. However, when the data turn to be non-Gaussian, the resulting performance can be strongly degraded. To fill this gap, I will first introduce the general framework of the Robust Estimation Theory, and then, I will show some recent results, applied to radar detection as well as Direction-of-Arrival estimation. The second part of the presentation will be devoted to the Random Matrix Theory (RMT) applied to radar/signal processing problems. Particularly, I will show recent results in Robust RMT that provide significant improvement in terms of estimation performance.