Interference Estimation and Mitigation for Stap Using the Two-dimensional Wold Decomposition Parametric Model

We develop parametric modeling and estimation methods for STAP data based on the results of the 2-D Wold-like decomposition. We show that the same parametric model that results from the 2-D Wold-like orthogonal decomposition naturally arises as the physical model in the problem of spacetime processing of airborne radar data. We exploit this correspondence to derive computationally efficient parametric fully adaptive and partially adaptive detection algorithms. Having estimated the parametric models of the noise and interference components of the field, the estimated parameters are substituted into the parametric expression of the covariance matrix to obtain an estimate of the interference-plus-noise covariance matrix. Hence the fully-adaptive weight vector is obtained. Moreover, it is proved that it is sufficient to estimate only the spectral support parameters of each interference component in order to obtain a projection matrix onto the subspace orthogonal to the interference subspace. The proposed partially adaptive parametric processing algorithm employs this property. The proposed parametric interference mitigation procedures can be applied even when only the information in a single range gate is available, thus achieving high performance gain when the data in the different range gates cannot be assumed stationary.