STAP training through knowledge-aided predictive modeling [radar signal processing]

In this paper, we investigate a spectral-domain approach to estimating the interference covariance matrix used in space-time adaptive processing. Traditionally, an estimate of the interference covariance matrix is obtained by averaging the space-time covariance matrices of multiple range bins. Unfortunately, the spectral content of these data snapshots usually varies, which corrupts the covariance estimate for the desired range. We propose to use knowledge sources to identify angle-Doppler spectral regions having the same underlying scattering statistics. Then, we use real-time data to form a synthetic aperture radar image, which is inherently an estimate of non-moving ground clutter. We then average the SAR pixels within each homogeneous region. The resulting clutter power map is used, along with knowledge of the radar system and scenario geometry, to compute the interference covariance matrix. Using simulated data, we demonstrate the potential performance of such a technique, demonstrate its dependence on accurate space-time steering vectors, and provide an example of using data to compensate for imperfect knowledge.