Approximate CFAR signal detection in strong low rank non-Gaussian interference

Abstract

We have devised a new generalized likelihood ratio test for detecting a signal in unknown, strong non-Gaussian low rank interference plus white Gaussian noise which needs no knowledge of the non-Gaussian distribution. From perturbation expansions of the test statistic, we establish the connection of the proposed GLRT detector to the UMPI test and show that it is approximately CFAR. Computer simulations indicate that the new detector significantly outperforms traditional adaptive methods in non-Gaussian interference.