MULTIVARIATE SPECTRAL RECONSTRUCTION OF STAP COVARIANCE MATRICES: HERMITIAN 'RELAXATION' AND PERFORMANCE ANALYSIS

摘要

In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter covariance matrix. In the reduced-order parametric matched filter STAP technique, this covariance matrix is reconstructed from a small number of estimated parameters, resulting in a much more efficient use of training samples. This paper explores a computationally advantageous "relaxed" maximum entropy (Burg) reconstruction technique which does not restore a strict Toeplitz-block structure, but does preserve the Burg spectrum. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset and compared with "proper" Toeplitz-block reconstruction.