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

Recent work suggests that the performance of conventional Gaussian-based adaptive methods can degrade severely in correlated non-Gaussian interference. We have addressed this problem by developing a new generalized likelihood ratio test (GLRT) for detecting a signal in unknown, strong non-Gaussian low rank interference plus white Gaussian noise which does not need detailed knowledge of the non-Gaussian distribution. The optimality of the proposed GLRT detector is established using perturbation expansions of the test statistic to show that it is closely related to the UMPI (uniformly most powerful invariant) test for this problem. Computer simulations indicate that the new detector significantly outperforms standard adaptive methods in non-Gaussian interference and is robust.