Robust Non-parametric Statistics Method for Joint Angle-Doppler Estimation in Non-Gaussian Clutter

In this paper, a new method is proposed for the problem of joint angle and Doppler estimation in non-Gaussian clutter which is modeled as the complex symmetric alpha stable (S alpha S) process. The proposed method normalizes each space-time snapshot vector by its infinity-norm, so that the second-order statistics-based superresolution estimators can become applicable to the non-Gaussian heavy-failed clutter environments. Unlike the well-known-fractional lower-order moment (FLOM)-based methods, the proposed method does not require any priori knowledge of the non-Gaussian clutter's statistics, and hence, it is "blind". Numerical results show that the proposed method outperforms the FLOM-based algorithms in the presence of non-Gaussian clutter.