Robust Covariance Matrix Estimation for STAP via Unscented Transformation

For the nonhomogeneous condition of space-time adaptive processing (STAP), a new unscented space-time adaptive processing algorithm is proposed that uses the unscented transformation (UT) to obtain the adequate number i.i.d training data and the approximative estimated covariance with the limited non-i.i.d data. By the proposed algorithm, the impact of the nonhomogeneous characteristic is reduced effectively. Simulation results indicate that the proposed algorithm can estimate the covariance in the nonhomogeneous condition exactly and has the favourable characteristic.