Knowledge-Aided Space-Time Adaptive Processing

A fundamental issue in knowledge-aided space-time adaptive processing (KA-STAP) is to determine the degree of accuracy of the a~priori knowledge and the optimal emphasis that should be placed on it. In KA-STAP, the a priori knowledge consists usually of an initial guess of the clutter covariance matrix. This can be obtained either by previous radar probings or by a map-based study. We consider a linear combination of the a~priori clutter covariance matrix with the sample covariance matrix obtained from secondary data, and derive an optimal weighting factor on the a priori knowledge by a maximum likelihood (ML) approach. The performance of the ML approach for KA-STAP is evaluated based on numerically simulated data.

I. Introduction and Preliminaries

Space-time adpative processing (STAP) is widely used in airborne radars for ground moving target detection [1]–[3]. In STAP, range bins are sampled during a coherent processing interval (CPI), and for each range bin the responses to pulses are collected from each of the elements of the receive antenna array. This data matrix for a certain range bin is then stacked column-wise to form an vector, which is called a space-time snapshot. It is well known that the optimal weight vector, , with being the degrees of freedom (Dofs) of STAP, used to maximize the signal-to-clutter-and-noise ratio (SCNR) of the beamformer output of this snapshot is given by [1] $$ {\bf w}={\bf R}^{-1}{\bf a}(\omega_{\rm s},\omega_{\rm D}) \eqno{\hbox{(1)}} $$where is the true clutter-and-noise covariance matrix for that particular range bin of current interest, and denotes the steering vector, which is a function of the spatial frequency and the (normalized) Doppler frequency of the target. Since a scaled version of above does not change the SCNR, we simplify our notation by considering the form in (1) only.